Release 0.10.5 (#1922 )

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: Support for google live api (#1905 )
2026-07-16 07:14:29 -04:00 · 2025-05-09 14:30:39 +07:00 · 2025-05-09 14:20:40 +07:00 · 2025-05-08 13:19:59 +07:00 · 2025-05-07 16:31:58 +07:00 · 2025-05-07 16:18:21 +07:00
237 changed files with 5997 additions and 6879 deletions
@@ -23,7 +23,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        node-version: [18.x, 20.x, 22.x, 23.x]
+        node-version: [20.x, 22.x, 23.x]
    name: E2E on Node.js ${{ matrix.node-version }}
    runs-on: ubuntu-latest
    steps:
@@ -53,7 +53,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        node-version: [18.x, 20.x, 22.x, 23.x]
+        node-version: [20.x, 22.x, 23.x]
    name: Test on Node.js ${{ matrix.node-version }}
    runs-on: ubuntu-latest
    steps:
@@ -1 +1 @@
-20
+22
@@ -1,5 +1,31 @@
 # @llamaindex/doc
 ## 0.2.17
 ### Patch Changes
 - Updated dependencies [9b2e25a]
  - @llamaindex/openai@0.3.6
  - @llamaindex/core@0.6.4
  - llamaindex@0.10.5
  - @llamaindex/cloud@4.0.6
  - @llamaindex/node-parser@2.0.4
  - @llamaindex/readers@3.1.2
  - @llamaindex/workflow@1.1.1
 ## 0.2.16
 ### Patch Changes
 - Updated dependencies [7e8e454]
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [bc53342]
 - Updated dependencies [41953a3]
  - @llamaindex/workflow@1.1.0
  - @llamaindex/cloud@4.0.5
  - llamaindex@0.10.4
 ## 0.2.15
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.15",
+  "version": "0.2.17",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -15,7 +15,7 @@
  "dependencies": {
    "@huggingface/transformers": "^3.5.0",
    "@icons-pack/react-simple-icons": "^10.1.0",
-    "@llama-flow/docs": "0.0.5",
+    "@llama-flow/docs": "0.0.8",
    "@llamaindex/chat-ui": "0.2.0",
    "@llamaindex/cloud": "workspace:*",
    "@llamaindex/core": "workspace:*",
@@ -26,7 +26,7 @@ const llm = openai();
 const response = await llm.chat({
  messages: [{ content: "Tell me a joke.", role: "user" }],
 });`,
-  `import { agent } from "llamaindex";
+  `import { agent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 const analyseAgent = agent({
@@ -36,7 +36,7 @@ const analyseAgent = agent({
 });
 const response = await analyseAgent.run(\`Analyse the given data:
 \${data}\`);`,
-  `import { agent, multiAgent } from "llamaindex";
+  `import { agent, multiAgent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 const analyseAgent = agent({
@@ -113,8 +113,9 @@ export default function HomePage() {
          description="Truly powerful retrieval-augmented generation applications use agentic techniques, and LlamaIndex.TS makes it easy to build them."
        >
          <CodeBlock
-            code={`import { agent, SimpleDirectoryReader, VectorStoreIndex } from "llamaindex";
+            code={`import { SimpleDirectoryReader, VectorStoreIndex } from "llamaindex";
 import { openai } from "@llamaindex/openai";
 import { agent } from "@llamaindex/workflow";
 // load documents from current directoy into an index
 const reader = new SimpleDirectoryReader();
@@ -9,10 +9,10 @@ To install llamaindex, run the following command:
 npm i llamaindex
 ```
-In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
+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:
 ```package-install
-npm i @llamaindex/openai
+npm i @llamaindex/openai @llamaindex/workflow
 ```
 Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
@@ -40,19 +40,7 @@ Make sure to set [moduleResolution](https://www.typescriptlang.org/docs/handbook
 }
 ```
-We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it, but with import path limitations.
+We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it.
 So you may encounter type errors when importing sub paths from the `llamaindex` package like:
 ```ts
 import { Settings } from "llamaindex";
 ```
 The simplest way to fix this without changing `moduleResolution` is to import directly from `llamaindex`:
 ```ts
 import { Settings } from "llamaindex";
 ```
 ## Enable AsyncIterable for `Web Stream` API
@@ -68,7 +56,8 @@ Some modules uses `Web Stream` API like `ReadableStream` and `WritableStream`, y
 ```
 ```typescript
-import { agent, tool } from 'llamaindex'
+import { tool } from 'llamaindex'
 import { agent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 Settings.llm = openai({
@@ -12,7 +12,8 @@ Agent Workflows are a powerful system that enables you to create and orchestrate
 The simplest use case is creating a single agent with specific tools. Here's an example of creating an assistant that tells jokes:
 ```typescript
-import { agent, tool } from "llamaindex";
+import { tool } from "llamaindex";
 import { agent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 // Define a joke-telling tool
@@ -40,17 +41,17 @@ console.log(result); // Baby Llama is called cria
 Agent Workflows provide a unified interface for event streaming, making it easy to track and respond to different events during execution:
 ```typescript
-import { AgentToolCall, AgentStream } from "llamaindex";
+import { agentToolCallEvent, agentStreamEvent } from "@llamaindex/workflow";
 // Get the workflow execution context
-const context = workflow.run("Tell me something funny");
+const events = workflow.runStream("Tell me something funny");
 // Stream and handle events
-for await (const event of context) {
+for await (const event of events) {
-  if (event instanceof AgentToolCall) {
+  if (agentToolCallEvent.include(event)) {
    console.log(`Tool being called: ${event.data.toolName}`);
  }
-  if (event instanceof AgentStream) {
+  if (agentStreamEvent.include(event)) {
    process.stdout.write(event.data.delta);
  }
 }
@@ -68,7 +69,8 @@ An Agent Workflow can orchestrate multiple agents, enabling complex interactions
 Here's an example of a multi-agent system that combines joke-telling and weather information:
 ```typescript
-import { multiAgent, agent, tool } from "llamaindex";
+import { tool } from "llamaindex";
 import { multiAgent, agent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 import { z } from "zod";
@@ -17,7 +17,8 @@ The `parameters` field in the tool configuration is defined using `zod`, a TypeS
 Example:
 ```ts
-import { agent, tool } from "llamaindex";
+import { tool } from "llamaindex";
 import { agent } from "@llamaindex/workflow";
 import { z } from "zod";
 // first arg is LLM input, second is bound arg
@@ -46,7 +47,7 @@ In this example, `z.object` is used to define a schema for the `parameters` wher
 You can import built-in tools from the `@llamaindex/tools` package.
 ```ts
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { wiki } from "@llamaindex/tools";
 const researchAgent = agent({
@@ -64,7 +65,7 @@ If you have a MCP server running, you can fetch tools from the server and use th
 ```ts
 // 1. Import MCP tools adapter
 import { mcp } from "@llamaindex/tools";
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 // 2. Initialize a MCP client
 // by npx
@@ -114,7 +115,8 @@ Note: calling the `bind` method will return a new `FunctionTool` instance, witho
 Example to pass a `userToken` as additional argument:
 ```ts
-import { agent, tool } from "llamaindex";
+import { tool } from "llamaindex";
 import { agent } from "@llamaindex/workflow";
 // first arg is LLM input, second is bound arg
 const queryKnowledgeBase = async ({ question }, { userToken }) => {
@@ -6,256 +6,13 @@ A `Workflow` in LlamaIndex is a lightweight, event-driven abstraction used to ch
 Workflows are designed to be flexible and can be used to build agents, RAG flows, extraction flows, or anything else you want to implement.
 To use workflows install this package:
 ```package-install
-npm i @llama-flow/core @llamaindex/openai
+npm i @llamaindex/workflow
 ```
-## Getting Started
+This package is a stable, production-ready version of our [llama-flow](../../../llamaflow) project. 
-Let's explore a simple workflow example where a joke is generated and then critiqued and iterated on:
+While you can still reference the llama-flow documentation for detailed information about the underlying concepts, we recommend using the `@llamaindex/workflow` package for all new projects to ensure stability and long-term availability.
 ```typescript
 import { OpenAI } from "@llamaindex/openai";
 import { createWorkflow, workflowEvent } from "@llama-flow/core";
 import { withStore } from "@llama-flow/core/middleware/store";
 // Create LLM instance
 const llm = new OpenAI({ model: "gpt-4.1-mini", apiKey: "..."});
 // Define our workflow events
 const startEvent = workflowEvent<string>(); // Input topic for joke
 const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
 const critiqueEvent = workflowEvent<{ joke: string, critique: string }>(); // Intermediate critique
 const resultEvent = workflowEvent<{ joke: string, critique: string }>(); // Final joke + critique
 // Create our workflow
 const jokeFlow = withStore(
    () => ({
        numIterations: 0,
        maxIterations: 3,
    }),
    createWorkflow()
 );
 // Define handlers for each step
 jokeFlow.handle([startEvent], async (event) => {
  // Prompt the LLM to write a joke
  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
  return jokeEvent.with({ joke: joke });
 });
 jokeFlow.handle([jokeEvent], async (event) => {
  // Prompt the LLM to critique the joke
  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
  const response = await llm.complete({ prompt });
  // If the critique includes "IMPROVE", keep iterating, else, return the result
  if (response.text.includes("IMPROVE")) {
    return critiqueEvent.with({ joke: event.data.joke, critique: response.text });
  }
  return resultEvent.with({ joke: event.data.joke, critique: response.text });
 });
 jokeFlow.handle([critiqueEvent], async (event) => {
  // Keep track of the number of iterations
  const store = jokeFlow.getStore();
  store.numIterations++;
  // Write a new joke based on the previous joke and critique
  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
  // If we've done less than the max number of iterations, keep iterating
  // else, return the result
  if (store.numIterations < store.maxIterations) {
    return jokeEvent.with({ joke: joke });
  }
  return resultEvent.with({ joke: joke, critique: event.data.critique });
 });
 // Usage
 async function main() {
  const { stream, sendEvent } = jokeFlow.createContext();
  sendEvent(startEvent.with("pirates"));
  let result: { joke: string, critique: string } | undefined;
  for await (const event of stream) {
    // console.log(event.data);  optionally log the event data
    if (resultEvent.include(event)) {
      result = event.data;
      break; // Stop when we get the final result
    }
  }
  console.log(result);
 }
 main().catch(console.error);
 ```
 There are a few moving pieces here, so let's go through this step by step.
 ### Defining Workflow Events
 ```typescript
 const startEvent = workflowEvent<string>(); // Input topic for joke
 const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
 const critiqueEvent = workflowEvent<{ joke: string, critique: string }>(); // Intermediate critique
 const resultEvent = workflowEvent<{ joke: string, critique: string }>(); // Final joke + critique
 ```
 Events are defined using the `workflowEvent` function and contain arbitrary data provided as a generic type. In this example, we have four events:
 - `startEvent`: Takes a string input (the joke topic)
 - `jokeEvent`: Contains an object with a joke property
 - `critiqueEvent`: Contains both the joke and its critique, used for the feedback loop
 - `resultEvent`: Contains the final joke and critique after any iterations
 ### Setting up the Workflow with Store Middleware
 ```typescript
 const jokeFlow = withStore(
    () => ({
        numIterations: 0,
        maxIterations: 3,
    }),
    createWorkflow()
 );
 ```
 Our workflow is implemented using the `createWorkflow()` function, enhanced with the `withStore` middleware. The store provides shared state across all handlers, which in this case tracks:
 - `numIterations`: Counts how many iterations of joke improvement we've done
 - `maxIterations`: Sets a limit to prevent infinite loops
 This store will be accesible within workflows by using the `jokeFlow.getStore()` function.
 ### Adding Handlers with Loops
 We have three key handlers in our workflow:
 1. The first handler processes the `startEvent`, generates an initial joke, and emits a `jokeEvent`:
 ```typescript
 jokeFlow.handle([startEvent], async (event) => {
  // Prompt the LLM to write a joke
  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
  return jokeEvent.with({ joke: joke });
 });
 ```
 2. The second handler handles the `jokeEvent`, critiques the joke, and either:
   - Emits a `critiqueEvent` if the joke needs improvement
   - Emits a `resultEvent` if the joke is good enough
 ```typescript
 jokeFlow.handle([jokeEvent], async (event) => {
  // Prompt the LLM to critique the joke
  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
  const response = await llm.complete({ prompt });
  // If the critique includes "IMPROVE", keep iterating, else, return the result
  if (response.text.includes("IMPROVE")) {
    return critiqueEvent.with({ joke: event.data.joke, critique: response.text });
  }
  return resultEvent.with({ joke: event.data.joke, critique: response.text });
 });
 ```
 3. The third handler processes the `critiqueEvent`, generates an improved joke based on the critique, and either:
   - Loops back to the joke evaluation (if under the iteration limit)
   - Emits the final `resultEvent` (if iteration limit reached)
 ```typescript
 jokeFlow.handle([critiqueEvent], async (event) => {
  // Keep track of the number of iterations
  const store = jokeFlow.getStore();
  store.numIterations++;
  // Write a new joke based on the previous joke and critique
  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
  // If we've done less than the max number of iterations, keep iterating
  // else, return the result
  if (store.numIterations < store.maxIterations) {
    return jokeEvent.with({ joke: joke });
  }
  return resultEvent.with({ joke: joke, critique: event.data.critique });
 });
 ```
 ### Running the Workflow
 ```typescript
 async function main() {
  const { stream, sendEvent } = jokeFlow.createContext();
  sendEvent(startEvent.with("pirates"));
  let result: { joke: string, critique: string } | undefined;
  for await (const event of stream) {
    // console.log(event.data);  optionally log the event data
    if (resultEvent.include(event)) {
      result = event.data;
      break; // Stop when we get the final result
    }
  }
  console.log(result);
 }
 ```
 To run the workflow, we:
 1. Create a workflow context with `createContext()`
 2. Trigger the initial event with `sendEvent()`
 3. Listen to the event stream and process events as they arrive
 4. Use `include()` to check if an event is of a specific type
 5. Break the loop when we receive our final result
 ### Using Stream Utilities
 Workflows provide utility functions to make working with event streams easier:
 ```typescript
 import { collect } from "@llama-flow/core/stream/consumer";
 import { until } from "@llama-flow/core/stream/until";
 // Create a workflow context and send the initial event
 const { stream, sendEvent } = jokeFlow.createContext();
 sendEvent(startEvent.with("pirates"));
 // Collect all events until we get a resultEvent
 const allEvents = await collect(until(stream, resultEvent));
 // The last event will be the resultEvent
 const finalEvent = allEvents[allEvents.length - 1];
 console.log(finalEvent.data); // Output the joke and critique
 ```
 The stream utilities make it easier to work with the asynchronous event flow. In this example, we use:
 - `collect`: Aggregates all events into an array
 - `until`: Creates a stream that emits events until a condition is met (in this case, until a resultEvent is received)
 You can combine these utilities with other stream operators like `filter` and `map` to create powerful processing pipelines.
 ## Next Steps
 To learn more about workflows, check out [the documentation in the tutorial section](../../../llamaflow).
@@ -120,11 +120,11 @@ async function main() {
 ```ts
 import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
-import { FunctionTool, LLMAgent } from "llamaindex";
+import { tool } from "llamaindex";
 import { agent } from "@llamaindex/workflow";
 import { z } from "zod";
-const sumNumbers = FunctionTool.from(
+const sumNumbers = tool(
  ({ a, b }: { a: number; b: number }) => `${a + b}`,
  {
    name: "sumNumbers",
    description: "Use this function to sum two numbers",
@@ -136,11 +136,11 @@ const sumNumbers = FunctionTool.from(
        description: "The second number",
      }),
    }),
    execute: ({ a, b }: { a: number; b: number }) => `${a + b}`,
  },
 );
-const divideNumbers = FunctionTool.from(
+const divideNumbers = tool(
  ({ a, b }: { a: number; b: number }) => `${a / b}`,
  {
    name: "divideNumbers",
    description: "Use this function to divide two numbers",
@@ -152,6 +152,7 @@ const divideNumbers = FunctionTool.from(
        description: "The divisor b to divide by",
      }),
    }),
    execute: ({ a, b }: { a: number; b: number }) => `${a / b}`,
  },
 );
@@ -161,15 +162,15 @@ const bedrock = new Bedrock({
 });
 async function main() {
-  const agent = new LLMAgent({
+  const myAgent = agent({
    llm: bedrock,
    tools: [sumNumbers, divideNumbers],
  });
-  const response = await agent.chat({
+  const response = await myAgent.run(
-    message: "How much is 5 + 5? then divide by 2",
+    "How much is 5 + 5? then divide by 2",
-  });
+  );
-  console.log(response.message);
+  console.log(response);
 }
 ```
@@ -15,7 +15,7 @@ In LlamaIndex, an agent is a semi-autonomous piece of software powered by an LLM
 You'll need to have a recent version of [Node.js](https://nodejs.org/en) installed. Then you can install LlamaIndex.TS by running
 ```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/readers @llamaindex/huggingface
+npm i llamaindex @llamaindex/openai @llamaindex/readers @llamaindex/huggingface @llamaindex/workflow
 ```
 ## Choose your model
@@ -35,11 +35,16 @@ First we'll need to pull in our dependencies. These are:
 import "dotenv/config";
 import {
  agent,
-  AgentStream,
+  agentStreamEvent,
  tool,
  openai,
 } from "@llamaindex/workflow";
 import {
  tool,
  Settings,
 } from "llamaindex";
 import {
  openai,
 } from "@llamaindex/openai";
 import { z } from "zod";
 ```
@@ -108,11 +113,10 @@ const myAgent = agent({ tools });
 ### Ask the agent a question
-We can use the `chat` interface to ask our agent a question, and it will use the tools we've defined to find an answer.
+We can use the `run` method to ask our agent a question, and it will use the tools we've defined to find an answer.
 ```javascript
-const context = myAgent.run("Sum 101 and 303");
+const result = await myAgent.run("Sum 101 and 303");
 const result = await context;
 console.log(result.data);
 ```
 You will see the following output:
@@ -123,12 +127,13 @@ You will see the following output:
 { result: 'The sum of 101 and 303 is 404.' }
 ```
-To stream the response, you can use the `AgentStream` event which provides chunks of the response as they become available. This allows you to display the response incrementally rather than waiting for the full response:
+To stream the response, you need to call `runStream`, which returns a stream of events. 
 The `agentStreamEvent` provides chunks of the response as they become available. This allows you to display the response incrementally rather than waiting for the full response:
 ```javascript
-const context = myAgent.run("Add 101 and 303");
+const events = myAgent.runStream("Add 101 and 303");
-for await (const event of context) {
+for await (const event of events) {
-  if (event instanceof AgentStream) {
+  if (agentStreamEvent.include(event)) {
    process.stdout.write(event.data.delta);
  }
 }
@@ -140,18 +145,18 @@ for await (const event of context) {
 The sum of 101 and 303 is 404.
 ```
 Note that we're filtering for `agentStreamEvent` as an agent might return other events - more about that in the following section.
 ### Logging workflow events
 To log the workflow events, you can check the event type and log the event data.
 ```javascript
-const context = myAgent.run("Sum 202 and 404");
+const events = myAgent.runStream("Sum 202 and 404");
-for await (const event of context) {
+for await (const event of events) {
-  if (event instanceof AgentStream) {
+  if (agentStreamEvent.include(event)) {
    // Stream the response
-    for (const chunk of event.data.delta) {
+    process.stdout.write(event.data.delta);
      process.stdout.write(chunk);
    }
  } else {
    // Log other events
    console.log("\nWorkflow event:", JSON.stringify(event, null, 2));
@@ -30,16 +30,16 @@ Settings.llm = ollama({
 ### Run local agent
-You can also create local agent by importing `agent` from `llamaindex`.
+You can also create local agent by importing `agent` from `@llamaindex/workflow`.
 ```javascript
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 const workflow = agent({
  tools: [getWeatherTool],
 });
-const workflowContext = workflow.run(
+const resutl = workflow.run(
  "What's the weather like in San Francisco?",
 );
 ```
@@ -25,7 +25,8 @@ We'll be bringing in `SimpleDirectoryReader`, `HuggingFaceEmbedding`, `VectorSto
 ```javascript
 import { QueryEngineTool, Settings, VectorStoreIndex } from "llamaindex";
-import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
+import { agent } from "@llamaindex/workflow";
 import { openai } from "@llamaindex/openai";
 import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
 ```
@@ -8,9 +8,10 @@ We have a comprehensive, step-by-step [guide to building agents in LlamaIndex.TS
 In a new folder:
-```bash npm2yarn
+```package-install
 npm init
 npm i -D typescript @types/node
 npm i @llamaindex/openai @llamaindex/workflow llamaindex zod
 ```
 ## Run agent
@@ -20,15 +21,14 @@ 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/agent/openai.ts</include>
+<include cwd>../../examples/agents/agent/openai.ts</include>
 To run the code:
-```bash
+```package-install
 npx tsx example.ts
 ```
@@ -36,9 +36,18 @@ You should expect output something like:
 ```
 {
-  content: 'The sum of 5 + 5 is 10. When you divide 10 by 2, you get 5.',
+  result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
-  role: 'assistant',
+  state: {
-  options: {}
+    memory: ChatMemoryBuffer {
      chatStore: SimpleChatStore {},
      chatStoreKey: 'chat_history',
      tokenLimit: 750000
    },
    scratchpad: [],
    currentAgentName: 'Agent',
    agents: [ 'Agent' ],
    nextAgentName: null
  }
 }
 Done
 ```
@@ -4,7 +4,7 @@
    "basic_agent",
    "rag",
    "agents",
-    "../../llamaflow",
+    "workflows",
    "local_llm",
    "chatbot",
    "structured_data_extraction"
@@ -16,7 +16,7 @@ LlamaIndex uses a two stage method when using an LLM with your data:
 1. **indexing stage**: preparing a knowledge base, and
 2. **querying stage**: retrieving relevant context from the knowledge to assist the LLM in responding to a question
-![](./_static/concepts/rag.jpg)
+![](/_static/concepts/rag.jpg)
 This process is also known as Retrieval Augmented Generation (RAG).
@@ -28,7 +28,7 @@ Let's explore each stage in detail.
 LlamaIndex.TS help you prepare the knowledge base with a suite of data connectors and indexes.
-![](./_static/concepts/indexing.jpg)
+![](/_static/concepts/indexing.jpg)
 [**Data Loaders**](/docs/llamaindex/modules/data/readers):
 A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).
@@ -54,7 +54,7 @@ LlamaIndex provides composable modules that help you build and integrate RAG pip
 These building blocks can be customized to reflect ranking preferences, as well as composed to reason over multiple knowledge bases in a structured way.
-![](./_static/concepts/querying.jpg)
+![](/_static/concepts/querying.jpg)
 #### Building Blocks
@@ -8,9 +8,10 @@ One of the most common use-cases for LlamaIndex is Retrieval-Augmented Generatio
 In a new folder, run:
-```bash npm2yarn
+```package-install
 npm init
 npm i -D typescript @types/node
 npm i llamaindex
 ```
 Then, check out the [installation](/docs/llamaindex/getting_started/installation) steps to install LlamaIndex.TS and prepare an OpenAI key.
@@ -34,7 +35,7 @@ Create a `tsconfig.json` file in the same folder:
 Now you can run the code with
-```bash
+```package-install
 npx tsx example.ts
 ```
@@ -10,9 +10,10 @@ You can use [other LLMs](/docs/llamaindex/modules/models/llms) via their APIs; i
 In a new folder:
-```bash npm2yarn
+```package-install
 npm init
 npm i -D typescript @types/node
 npm i @llamaindex/openai zod
 ```
 ## Extract data
@@ -27,7 +28,7 @@ Create the file `example.ts`. This code will:
 To run the code:
-```bash
+```package-install
 npx tsx example.ts
 ```
@@ -0,0 +1,176 @@
 ---
 title: Workflows
 ---
 A `Workflow` in LlamaIndex is a lightweight, event-driven abstraction used to chain together several events. Workflows are made up of `handlers`, with each one responsible for processing specific event types and emitting new events.
 Workflows are designed to be flexible and can be used to build agents, RAG flows, extraction flows, or anything else you want to implement.
 ```package-install
 npm i @llamaindex/workflow @llamaindex/openai
 ```
 ## Getting Started
 Let's explore a simple workflow example where a joke is generated and then critiqued and iterated on:
 <include cwd>../../examples/agents/workflow/joke.ts</include>
 There are a few moving pieces here, so let's go through this step by step.
 ### Defining Workflow Events
 ```typescript
 const startEvent = workflowEvent<string>(); // Input topic for joke
 const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
 const critiqueEvent = workflowEvent<{ joke: string; critique: string }>(); // Intermediate critique
 const resultEvent = workflowEvent<{ joke: string; critique: string }>(); // Final joke + critique
 ```
 Events are defined using the `workflowEvent` function and contain arbitrary data provided as a generic type. In this example, we have four events:
 - `startEvent`: Takes a string input (the joke topic)
 - `jokeEvent`: Contains an object with a joke property
 - `critiqueEvent`: Contains both the joke and its critique, used for the feedback loop
 - `resultEvent`: Contains the final joke and critique after any iterations
 ### Setting up the Workflow with Stateful Middleware
 ```typescript
 const { withState, getContext } = createStatefulMiddleware(() => ({
  numIterations: 0,
  maxIterations: 3,
 }));
 const jokeFlow = withState(createWorkflow());
 ```
 Our workflow is implemented using the `createWorkflow()` function, enhanced with the `withState` middleware. This middleware provides shared state across all handlers, which in this case tracks:
 - `numIterations`: Counts how many iterations of joke improvement we've done
 - `maxIterations`: Sets a limit to prevent infinite loops
 This state will be accessible within workflows by using the `getContext().state` function.
 ### Adding Handlers with Loops
 We have three key handlers in our workflow:
 1. The first handler processes the `startEvent`, generates an initial joke, and emits a `jokeEvent`:
 ```typescript
 jokeFlow.handle([startEvent], async (event) => {
  // Prompt the LLM to write a joke
  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke =
    response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ??
    response.text;
  return jokeEvent.with({ joke: joke });
 });
 ```
 2. The second handler handles the `jokeEvent`, critiques the joke, and either:
   - Emits a `critiqueEvent` if the joke needs improvement
   - Emits a `resultEvent` if the joke is good enough
 ```typescript
 jokeFlow.handle([jokeEvent], async (event) => {
  // Prompt the LLM to critique the joke
  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
  const response = await llm.complete({ prompt });
  // If the critique includes "IMPROVE", keep iterating, else, return the result
  if (response.text.includes("IMPROVE")) {
    return critiqueEvent.with({
      joke: event.data.joke,
      critique: response.text,
    });
  }
  return resultEvent.with({ joke: event.data.joke, critique: response.text });
 });
 ```
 3. The third handler processes the `critiqueEvent`, generates an improved joke based on the critique, and either:
   - Loops back to the joke evaluation (if under the iteration limit)
   - Emits the final `resultEvent` (if iteration limit reached)
 ```typescript
 jokeFlow.handle([critiqueEvent], async (event) => {
  // Keep track of the number of iterations
  const state = getContext().state;
  state.numIterations++;
  // Write a new joke based on the previous joke and critique
  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke =
    response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ??
    response.text;
  // If we've done less than the max number of iterations, keep iterating
  // else, return the result
  if (state.numIterations < state.maxIterations) {
    return jokeEvent.with({ joke: joke });
  }
  return resultEvent.with({ joke: joke, critique: event.data.critique });
 });
 ```
 ### Running the Workflow
 ```typescript
 async function main() {
  const { stream, sendEvent } = jokeFlow.createContext();
  sendEvent(startEvent.with("pirates"));
  let result: { joke: string, critique: string } | undefined;
  for await (const event of stream) {
    // console.log(event.data);  optionally log the event data
    if (resultEvent.include(event)) {
      result = event.data;
      break; // Stop when we get the final result
    }
  }
  console.log(result);
 }
 ```
 To run the workflow, we:
 1. Create a workflow context with `createContext()`
 2. Trigger the initial event with `sendEvent()`
 3. Listen to the event stream and process events as they arrive
 4. Use `include()` to check if an event is of a specific type
 5. Break the loop when we receive our final result
 ### Using Stream Utilities
 The `stream` returned by `createContext` contains utility functions to make working with event streams easier:
 ```typescript
 // Create a workflow context and send the initial event
 const { stream, sendEvent } = jokeFlow.createContext();
 sendEvent(startEvent.with("pirates"));
 // Collect all events until we get a resultEvent
 const allEvents = await stream.until(resultEvent).toArray(); 
 // The last event will be the resultEvent
 const finalEvent = allEvents.at(-1);
 console.log(finalEvent.data); // Output the joke and critique
 ```
 The stream utilities make it easier to work with the asynchronous event flow. In this example, we use:
 - `toArray`: Aggregates all events into an array
 - `until`: Creates a stream that emits events until a condition is met (in this case, until a resultEvent is received)
 You can combine these utilities with other stream operators like `filter` and `map` to create powerful processing pipelines.
 ## Next Steps
 To learn more about workflows, check out [the Workflows documentation](/docs/llamaindex/modules/agents/workflows).
@@ -1,3 +1,3 @@
 {
-  "pages": ["llamaindex", "api"]
+  "pages": ["llamaindex", "api", "llamaflow"]
 }
@@ -1,5 +1,20 @@
 # @llamaindex/cloudflare-worker-agent-test
 ## 0.0.159
 ### Patch Changes
 - llamaindex@0.10.5
 ## 0.0.158
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.0.157
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.157",
+  "version": "0.0.159",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,5 +1,18 @@
 # @llamaindex/llama-parse-browser-test
 ## 0.0.61
 ### Patch Changes
 - @llamaindex/cloud@4.0.6
 ## 0.0.60
 ### Patch Changes
 - Updated dependencies [2225ffd]
  - @llamaindex/cloud@4.0.5
 ## 0.0.59
 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/llama-parse-browser-test",
  "private": true,
-  "version": "0.0.59",
+  "version": "0.0.61",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -1,5 +1,20 @@
 # @llamaindex/next-agent-test
 ## 0.1.159
 ### Patch Changes
 - llamaindex@0.10.5
 ## 0.1.158
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.1.157
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-agent-test",
-  "version": "0.1.157",
+  "version": "0.1.159",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,20 @@
 # test-edge-runtime
 ## 0.1.158
 ### Patch Changes
 - llamaindex@0.10.5
 ## 0.1.157
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.1.156
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.156",
+  "version": "0.1.158",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,22 @@
 # @llamaindex/next-node-runtime
 ## 0.1.26
 ### Patch Changes
 - llamaindex@0.10.5
 - @llamaindex/huggingface@0.1.8
 - @llamaindex/readers@3.1.2
 ## 0.1.25
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.1.24
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.1.24",
+  "version": "0.1.26",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,20 @@
 # vite-import-llamaindex
 ## 0.0.25
 ### Patch Changes
 - llamaindex@0.10.5
 ## 0.0.24
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.0.23
 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "vite-import-llamaindex",
  "private": true,
-  "version": "0.0.23",
+  "version": "0.0.25",
  "type": "module",
  "scripts": {
    "build": "vite build",
@@ -1,5 +1,22 @@
 # @llamaindex/waku-query-engine-test
 ## 0.0.159
 ### Patch Changes
 - Updated dependencies [9b2e25a]
  - @llamaindex/env@0.1.30
  - llamaindex@0.10.5
 ## 0.0.158
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 0.0.157
 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.157",
+  "version": "0.0.159",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,5 +1,59 @@
 # examples
 ## 0.3.14
 ### Patch Changes
 - 9b2e25a: Use Uint8Array instead of Buffer for file type messages (works with non-NodeJS)
 - 206b491: Add support for google live api
 - Updated dependencies [9b2e25a]
 - Updated dependencies [206b491]
  - @llamaindex/anthropic@0.3.5
  - @llamaindex/google@0.3.0
  - @llamaindex/openai@0.3.6
  - @llamaindex/core@0.6.4
  - @llamaindex/env@0.1.30
  - llamaindex@0.10.5
  - @llamaindex/clip@0.0.54
  - @llamaindex/deepinfra@0.0.54
  - @llamaindex/deepseek@0.0.14
  - @llamaindex/fireworks@0.0.14
  - @llamaindex/groq@0.0.69
  - @llamaindex/huggingface@0.1.8
  - @llamaindex/jinaai@0.0.14
  - @llamaindex/perplexity@0.0.11
  - @llamaindex/azure@0.1.14
  - @llamaindex/elastic-search@0.1.4
  - @llamaindex/milvus@0.1.13
  - @llamaindex/qdrant@0.1.13
  - @llamaindex/supabase@0.1.3
  - @llamaindex/together@0.0.14
  - @llamaindex/vllm@0.0.40
  - @llamaindex/cloud@4.0.6
  - @llamaindex/node-parser@2.0.4
  - @llamaindex/assemblyai@0.1.3
  - @llamaindex/cohere@0.0.18
  - @llamaindex/discord@0.1.3
  - @llamaindex/mistral@0.1.4
  - @llamaindex/mixedbread@0.0.18
  - @llamaindex/notion@0.1.3
  - @llamaindex/ollama@0.1.4
  - @llamaindex/portkey-ai@0.0.46
  - @llamaindex/replicate@0.0.46
  - @llamaindex/astra@0.0.18
  - @llamaindex/chroma@0.0.18
  - @llamaindex/firestore@1.0.11
  - @llamaindex/mongodb@0.0.19
  - @llamaindex/pinecone@0.1.4
  - @llamaindex/postgres@0.0.47
  - @llamaindex/upstash@0.0.18
  - @llamaindex/weaviate@0.0.18
  - @llamaindex/vercel@0.1.4
  - @llamaindex/voyage-ai@1.0.10
  - @llamaindex/readers@3.1.2
  - @llamaindex/tools@0.0.9
  - @llamaindex/workflow@1.1.1
 ## 0.3.13
 ### Patch Changes
@@ -1,12 +1,12 @@
 import { tool } from "@llamaindex/core/tools";
 import { openai } from "@llamaindex/openai";
 import fs from "fs";
 import {
  agent,
-  AgentToolCall,
+  agentToolCallEvent,
-  AgentToolCallResult,
+  agentToolCallResultEvent,
  multiAgent,
-  tool,
+} from "@llamaindex/workflow";
-} from "llamaindex";
+import fs from "fs";
 import os from "os";
 import { z } from "zod";
@@ -56,19 +56,19 @@ async function main() {
    rootAgent: researchAgent,
  });
-  const context = workflow.run("Write a blog post about history of LLM");
+  const events = workflow.runStream("Write a blog post about history of LLM");
  let finalResult;
-  for await (const event of context) {
+  for await (const event of events) {
-    if (event instanceof AgentToolCall) {
+    if (agentToolCallEvent.include(event)) {
      console.log(
-        `[Agent ${event.displayName}] executing tool ${event.data.toolName} with parameters ${JSON.stringify(
+        `[Agent ${event.data.agentName}] executing tool ${event.data.toolName} with parameters ${JSON.stringify(
          event.data.toolKwargs,
        )}`,
      );
-    } else if (event instanceof AgentToolCallResult) {
+    } else if (agentToolCallResultEvent.include(event)) {
      console.log(
-        `[Agent ${event.displayName}] executed tool ${event.data.toolName} with result ${event.data.toolOutput.result}`,
+        `[Tool ${event.data.toolName}] executed with result ${event.data.toolOutput.result}`,
      );
    }
    finalResult = event;
@@ -1,5 +1,6 @@
 import { OpenAI } from "@llamaindex/openai";
-import { FunctionTool, agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const csvData =
@@ -11,24 +12,22 @@ const userQuestion = "which are the best comedies after 2010?";
  // The agent will succeed if we increase `maxTokens` to 1024
  const llm = new OpenAI({ model: "gpt-4-turbo", maxTokens: 1024 });
-  const interpreterTool = FunctionTool.from(
+  const interpreterTool = tool({
-    ({ code }) => {
+    name: "interpreter",
    description:
      "Execute python code in a Jupyter notebook cell and return any result, stdout, stderr, display_data, and error.",
    parameters: z.object({
      code: z.string({
        description: "The python code to execute in a single cell.",
      }),
    }),
    execute: ({ code }) => {
      console.log(
        `To answer the user's question, call the following code:\n${code}`,
      );
      return code;
    },
-    {
+  });
      name: "interpreter",
      description:
        "Execute python code in a Jupyter notebook cell and return any result, stdout, stderr, display_data, and error.",
      parameters: z.object({
        code: z.string({
          description: "The python code to execute in a single cell.",
        }),
      }),
    },
  );
  const systemPrompt =
    "You are a Python interpreter.\n        - You are given tasks to complete and you run python code to solve them.\n        - The python code runs in a Jupyter notebook. Every time you call $(interpreter) tool, the python code is executed in a separate cell. It's okay to make multiple calls to $(interpreter).\n        - Display visualizations using matplotlib or any other visualization library directly in the notebook. Shouldn't save the visualizations to a file, just return the base64 encoded data.\n        - You can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n        - You can run any python code you want in a secure environment.";
@@ -1,6 +1,6 @@
 import { openai } from "@llamaindex/openai";
 import { mcp } from "@llamaindex/tools";
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 async function main() {
  // Create an MCP server for filesystem tools
@@ -6,15 +6,15 @@
 import { openai } from "@llamaindex/openai";
 import {
  agent,
-  AgentInput,
+  agentInputEvent,
-  AgentOutput,
+  agentOutputEvent,
-  AgentStream,
+  agentStreamEvent,
-  AgentToolCall,
+  agentToolCallEvent,
-  AgentToolCallResult,
+  agentToolCallResultEvent,
  multiAgent,
-  StopEvent,
+  stopAgentEvent,
-  tool,
+} from "@llamaindex/workflow";
-} from "llamaindex";
+import { tool } from "llamaindex";
 import { z } from "zod";
 const llm = openai({
@@ -79,21 +79,21 @@ async function multiWeatherAgent() {
  });
  // Ask the agent to get the weather in a city
-  const context = workflow.run(
+  const events = workflow.runStream(
    "What is the weather in San Francisco in Celsius?",
  );
  // Stream the events
-  for await (const event of context) {
+  for await (const event of events) {
-    // These events might be useful for UI
+    // These events are useful for reporting the current state to the user in the UI
    if (
-      event instanceof AgentToolCall ||
+      agentToolCallEvent.include(event) ||
-      event instanceof AgentToolCallResult ||
+      agentToolCallResultEvent.include(event) ||
-      event instanceof AgentOutput ||
+      agentOutputEvent.include(event) ||
-      event instanceof AgentInput ||
+      agentInputEvent.include(event) ||
-      event instanceof StopEvent
+      stopAgentEvent.include(event)
    ) {
      console.log(event);
-    } else if (event instanceof AgentStream) {
+    } else if (agentStreamEvent.include(event)) {
      for (const chunk of event.data.delta) {
        process.stdout.write(chunk);
      }
@@ -1,5 +1,6 @@
 import { openai } from "@llamaindex/openai";
-import { agent, tool } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const sumNumbers = tool({
@@ -25,7 +26,7 @@ const divideNumbers = tool({
 async function main() {
  const mathAgent = agent({
    tools: [sumNumbers, divideNumbers],
-    llm: openai({ model: "gpt-4o-mini" }),
+    llm: openai({ model: "gpt-4.1-mini" }),
    verbose: false,
  });
@@ -1,11 +1,9 @@
 import {
  AgentStream,
  AgentToolCallResult,
  Document,
  VectorStoreIndex,
  agent,
-  openai,
+  agentStreamEvent,
-} from "llamaindex";
+  agentToolCallResultEvent,
 } from "@llamaindex/workflow";
 import { Document, VectorStoreIndex, openai } from "llamaindex";
 async function main() {
  const index = await VectorStoreIndex.fromDocuments([
@@ -30,15 +28,15 @@ async function main() {
    ],
  });
-  const context = myAgent.run("The fact about cats");
+  const events = myAgent.runStream("The fact about cats");
-  for await (const event of context) {
+  for await (const event of events) {
-    if (event instanceof AgentToolCallResult) {
+    if (agentToolCallResultEvent.include(event)) {
      console.log(
        "Using these retrieved information to answer the question:\n",
        event.data.toolOutput.result,
      );
-    } else if (event instanceof AgentStream) {
+    } else if (agentStreamEvent.include(event)) {
      for (const chunk of event.data.delta) {
        process.stdout.write(chunk);
      }
@@ -2,8 +2,8 @@
 * This example shows how to use a single agent with a tool
 */
 import { openai } from "@llamaindex/openai";
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
-import { getWeatherTool } from "../agent/utils/tools";
+import { getWeatherTool } from "../deprecated/utils/tools";
 async function main() {
  const weatherAgent = agent({
@@ -14,14 +14,14 @@ async function main() {
    verbose: false,
  });
-  // Run the agent and keep the context
+  const result = await weatherAgent.run(
-  const context = weatherAgent.run("What's the weather like in San Francisco?");
+    "What's the weather like in San Francisco?",
-  const result = await context;
+  );
  console.log(`${JSON.stringify(result, null, 2)}`);
-  // Reuse the context from the previous run
+  // Reuse the state from the previous run
  const caResult = await weatherAgent.run("Compare it with California?", {
-    context: context.data,
+    state: result.data.state,
  });
  console.log(`${JSON.stringify(caResult, null, 2)}`);
 }
@@ -1,6 +1,6 @@
 import { OpenAI } from "@llamaindex/openai";
 import { wiki } from "@llamaindex/tools";
-import { AgentStream, agent } from "llamaindex";
+import { agent, agentStreamEvent } from "@llamaindex/workflow";
 async function main() {
  const llm = new OpenAI({ model: "gpt-4-turbo" });
@@ -12,10 +12,10 @@ async function main() {
  });
  // Chat with the agent
-  const context = workflow.run("Who was Goethe?");
+  const events = workflow.runStream("Who was Goethe?");
-  for await (const event of context) {
+  for await (const event of events) {
-    if (event instanceof AgentStream) {
+    if (agentStreamEvent.include(event)) {
      for (const chunk of event.data.delta) {
        process.stdout.write(chunk);
      }
@@ -1,11 +1,11 @@
 import fs from "fs";
 import {
  agent,
-  AgentToolCall,
+  agentToolCallEvent,
-  AgentToolCallResult,
+  agentToolCallResultEvent,
  multiAgent,
-  tool,
+} from "@llamaindex/workflow";
-} from "llamaindex";
+import fs from "fs";
 import { tool } from "llamaindex";
 import { z } from "zod";
 import { anthropic } from "@llamaindex/anthropic";
@@ -81,21 +81,21 @@ async function main() {
    rootAgent: researchAgent,
  });
-  const context = workflow.run(
+  const events = workflow.runStream(
    "Write a report about New York weather and inflation",
  );
  let finalResult;
-  for await (const event of context) {
+  for await (const event of events) {
-    if (event instanceof AgentToolCall) {
+    if (agentToolCallEvent.include(event)) {
      console.log(
-        `[Agent ${event.displayName}] executing tool ${event.data.toolName} with parameters ${JSON.stringify(
+        `[Agent ${event.data.agentName}] executing tool ${event.data.toolName} with parameters ${JSON.stringify(
          event.data.toolKwargs,
        )}`,
      );
-    } else if (event instanceof AgentToolCallResult) {
+    } else if (agentToolCallResultEvent.include(event)) {
      console.log(
-        `[Agent ${event.displayName}] executed tool ${event.data.toolName} with result ${event.data.toolOutput.result}`,
+        `[Agent executed tool ${event.data.toolName} with result ${event.data.toolOutput.result}`,
      );
    }
    finalResult = event;
@@ -1,6 +1,6 @@
 import { ollama } from "@llamaindex/ollama";
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
-import { getWeatherTool } from "../agent/utils/tools";
+import { getWeatherTool } from "../deprecated/utils/tools";
 async function main() {
  const myAgent = agent({
@@ -0,0 +1,94 @@
 import { openai } from "@llamaindex/openai";
 import {
  createStatefulMiddleware,
  createWorkflow,
  workflowEvent,
 } from "@llamaindex/workflow";
 // Create LLM instance
 const llm = openai({ model: "gpt-4.1-mini" });
 // Define our workflow events
 const startEvent = workflowEvent<string>(); // Input topic for joke
 const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
 const critiqueEvent = workflowEvent<{ joke: string; critique: string }>(); // Intermediate critique
 const resultEvent = workflowEvent<{ joke: string; critique: string }>(); // Final joke + critique
 // Create our workflow
 const { withState, getContext } = createStatefulMiddleware(() => ({
  numIterations: 0,
  maxIterations: 3,
 }));
 const jokeFlow = withState(createWorkflow());
 // Define handlers for each step
 jokeFlow.handle([startEvent], async (event) => {
  // Prompt the LLM to write a joke
  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke =
    response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ??
    response.text;
  return jokeEvent.with({ joke: joke });
 });
 jokeFlow.handle([jokeEvent], async (event) => {
  // Prompt the LLM to critique the joke
  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
  const response = await llm.complete({ prompt });
  // If the critique includes "IMPROVE", keep iterating, else, return the result
  if (response.text.includes("IMPROVE")) {
    return critiqueEvent.with({
      joke: event.data.joke,
      critique: response.text,
    });
  }
  return resultEvent.with({ joke: event.data.joke, critique: response.text });
 });
 jokeFlow.handle([critiqueEvent], async (event) => {
  // Keep track of the number of iterations
  const state = getContext().state;
  state.numIterations++;
  // Write a new joke based on the previous joke and critique
  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
  const response = await llm.complete({ prompt });
  // Parse the joke from the response
  const joke =
    response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ??
    response.text;
  // If we've done less than the max number of iterations, keep iterating
  // else, return the result
  if (state.numIterations < state.maxIterations) {
    return jokeEvent.with({ joke: joke });
  }
  return resultEvent.with({ joke: joke, critique: event.data.critique });
 });
 // Usage
 async function main() {
  const { stream, sendEvent } = jokeFlow.createContext();
  sendEvent(startEvent.with("pirates"));
  let result: { joke: string; critique: string } | undefined;
  for await (const event of stream) {
    // console.log(event.data);  optionally log the event data
    if (resultEvent.include(event)) {
      result = event.data;
      break; // Stop when we get the final result
    }
  }
  console.log(result);
 }
 main().catch(console.error);
@@ -1,6 +1,7 @@
 import { anthropic } from "@llamaindex/anthropic";
 import { wiki } from "@llamaindex/tools";
-import { agent, tool } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const workflow = agent({
@@ -25,7 +25,7 @@ async function main() {
          },
          {
            type: "file",
-            data: fs.readFileSync("./data/manga.pdf"),
+            data: Uint8Array.from(fs.readFileSync("./data/manga.pdf")),
            mimeType: "application/pdf",
          },
        ],
@@ -1,5 +1,6 @@
 import { gemini, GEMINI_MODEL } from "@llamaindex/google";
-import { agent, tool } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const sumNumbers = tool({
@@ -32,7 +32,7 @@ import fs from "fs";
          },
          {
            type: "file",
-            data: fs.readFileSync("./data/manga.pdf"),
+            data: Uint8Array.from(fs.readFileSync("./data/manga.pdf")),
            mimeType: "application/pdf",
          },
        ],
@@ -0,0 +1,104 @@
 import { ModalityType } from "@llamaindex/core/schema";
 import { tool } from "@llamaindex/core/tools";
 import { gemini, GEMINI_MODEL } from "@llamaindex/google";
 import { liveEvents } from "llamaindex";
 import { z } from "zod";
 const weatherTool = tool({
  name: "weather",
  description: "Get the weather",
  parameters: z.object({
    location: z.string({
      description: "The location to get the weather for",
    }),
  }),
  execute: ({ location }) => {
    return `The weather in ${location} is rainy`;
  },
 });
 const divideNumbers = tool({
  name: "divideNumbers",
  description: "Use this function to divide two numbers",
  parameters: z.object({
    a: z.number().describe("The dividend a to divide"),
    b: z.number().describe("The divisor b to divide by"),
  }),
  execute: ({ a, b }) => `${a / b}`,
 });
 async function main() {
  const apiKey = process.env.GOOGLE_API_KEY;
  if (!apiKey) {
    console.error(
      "Please set GOOGLE_API_KEY in your environment variables or .env file",
    );
    process.exit(1);
  }
  console.log("🚀 Initializing Gemini Live API with tools example...");
  const llm = gemini({
    apiKey: apiKey,
    model: GEMINI_MODEL.GEMINI_2_0_FLASH_LIVE, // Must use a live-compatible model
  });
  console.log("📡 Connecting to Gemini Live session...");
  // Connect to a live session with tools
  const session = await llm.live.connect({
    // Specify response modalities (text response is required for tools)
    responseModality: [ModalityType.TEXT],
    // Register our tools with the session
    tools: [weatherTool, divideNumbers],
    // Optional system instruction
    systemInstruction:
      "You are a helpful assistant that can use tools. When answering questions about weather or divide numbers, always use the appropriate tool.",
  });
  (async () => {
    console.log("🎧 Listening for events...");
    for await (const event of session.streamEvents()) {
      if (liveEvents.open.include(event)) {
        console.log("✅ Connected to Gemini Live session");
        console.log(
          "💬 Sending message: 'What's the weather in San Francisco and what is 100 / 2?'",
        );
        session.sendMessage({
          content: "What's the weather in San Francisco and what is 100 / 2?",
          role: "user",
        });
      } else if (liveEvents.text.include(event)) {
        process.stdout.write(event.text);
      } else if (liveEvents.error.include(event)) {
        console.error("❌ Error:", event.error);
      } else if (liveEvents.close.include(event)) {
        console.log("👋 Session closed");
        process.exit(0);
      } else if (liveEvents.setupComplete.include(event)) {
        console.log("🔧 Setup complete");
      }
    }
  })();
  process.on("SIGINT", async () => {
    console.log("\n👋 Interrupted by user. Closing session...");
    await session.disconnect();
    process.exit(0);
  });
  // Timeout after 2 minutes if no interaction
  setTimeout(async () => {
    console.log("\n⏱️ Session timeout. Closing session...");
    await session.disconnect();
    process.exit(0);
  }, 120000);
 }
 main().catch((error) => {
  console.error("❌ Fatal error:", error);
  process.exit(1);
 });
@@ -0,0 +1,221 @@
 import { fs } from "@llamaindex/env";
 import { gemini, GEMINI_MODEL, GeminiLiveSession } from "@llamaindex/google";
 import { liveEvents } from "llamaindex";
 import path from "path";
 function createWavHeader(
  sampleRate = 16000,
  bitsPerSample = 16,
  channels = 1,
  dataLength: number,
 ) {
  const buffer = Buffer.alloc(44);
  // RIFF chunk descriptor
  buffer.write("RIFF", 0);
  buffer.writeUInt32LE(36 + dataLength, 4); // File size - 8
  buffer.write("WAVE", 8);
  // fmt sub-chunk
  buffer.write("fmt ", 12);
  buffer.writeUInt32LE(16, 16); // Subchunk1Size (16 for PCM)
  buffer.writeUInt16LE(1, 20); // AudioFormat (1 for PCM)
  buffer.writeUInt16LE(channels, 22); // NumChannels
  buffer.writeUInt32LE(sampleRate, 24); // SampleRate
  buffer.writeUInt32LE((sampleRate * channels * bitsPerSample) / 8, 28); // ByteRate
  buffer.writeUInt16LE((channels * bitsPerSample) / 8, 32); // BlockAlign
  buffer.writeUInt16LE(bitsPerSample, 34); // BitsPerSample
  // data sub-chunk
  buffer.write("data", 36);
  buffer.writeUInt32LE(dataLength, 40); // Subchunk2Size
  return buffer;
 }
 async function saveWavFile(
  audioChunks: Buffer[],
  filePath: string,
  sampleRate = 16000,
  bitsPerSample = 16,
  channels = 1,
 ): Promise<void> {
  if (audioChunks.length === 0) {
    throw new Error("No audio data to save");
  }
  try {
    const combinedAudioData = Buffer.concat(audioChunks);
    console.log(`Total audio data: ${combinedAudioData.length} bytes`);
    const wavHeader = createWavHeader(
      sampleRate,
      bitsPerSample,
      channels,
      combinedAudioData.length,
    );
    const wavFile = Buffer.concat([wavHeader, combinedAudioData]);
    await fs.writeFile(filePath, wavFile);
    console.log(`💾 Saved audio to ${filePath}`);
    return;
  } catch (error) {
    console.error("❌ Error saving audio file:", error);
    throw error;
  }
 }
 async function main() {
  const apiKey = process.env.GOOGLE_API_KEY;
  if (!apiKey) {
    console.error(
      "Please set GOOGLE_API_KEY in your environment variables or .env file",
    );
    process.exit(1);
  }
  console.log("🚀 Initializing Gemini Live API example...");
  const llm = gemini({
    model: GEMINI_MODEL.GEMINI_2_0_FLASH_LIVE,
    voiceName: "Zephyr",
  });
  console.log("📡 Connecting to Gemini Live session...");
  const session = await llm.live.connect();
  let isRunning = true;
  const audioChunks: Buffer[] = [];
  let audioResponse = false;
  (async () => {
    try {
      console.log("🎧 Listening for events...");
      for await (const event of session.streamEvents()) {
        if (liveEvents.open.include(event)) {
          console.log("✅ Connected to Gemini Live session");
          console.log(
            "💬 Sending text message: 'Say something about you for 10 seconds'",
          );
          session.sendMessage({
            content: "Say something about you for 10 seconds",
            role: "user",
          });
          setTimeout(() => {
            sendPcmAudioFile(session);
          }, 3000);
        } else if (liveEvents.setupComplete.include(event)) {
          console.log("✅ Setup complete");
        } else if (liveEvents.text.include(event)) {
          process.stdout.write(event.text);
        } else if (liveEvents.audio.include(event)) {
          console.log("\n🔊 Received audio chunk");
          audioResponse = true;
          try {
            const chunk = Buffer.from(event.data as string, "base64");
            audioChunks.push(chunk);
            console.log(`Received audio chunk: ${chunk.length} bytes`);
          } catch (error) {
            console.error("❌ Error processing audio chunk:", error);
          }
        } else if (liveEvents.error.include(event)) {
          console.error("❌ Error:", event.error);
        } else if (liveEvents.close.include(event)) {
          console.log("👋 Session closed");
          if (audioResponse && audioChunks.length > 0) {
            try {
              await saveWavFile(audioChunks, "gemini-response.wav");
            } catch (error) {
              console.error("❌ Error saving final audio file:", error);
            }
          }
          isRunning = false;
          break;
        }
      }
    } catch (error) {
      console.error("❌ Error processing stream:", error);
    }
  })();
  async function sendPcmAudioFile(session: GeminiLiveSession) {
    try {
      console.log("🎤 Reading PCM audio file...");
      const filePath = path.join(__dirname, "hello_are_you_there.pcm");
      console.log(`Reading file from: ${filePath}`);
      const audioBuffer = await fs.readFile(filePath);
      const base64Audio = audioBuffer.toString("base64");
      session.sendMessage({
        content: [
          {
            type: "audio",
            data: base64Audio,
            mimeType: "audio/pcm;rate=16000",
          },
        ],
        role: "user",
      });
      console.log("🎤 PCM audio file sent! Waiting for response...");
    } catch (error) {
      console.error("❌ Error sending audio file:", error);
    }
  }
  setTimeout(async () => {
    console.log("\n⏱️ Time's up! Closing session...");
    if (audioResponse && audioChunks.length > 0) {
      try {
        await saveWavFile(audioChunks, "gemini-response.wav");
      } catch (error) {
        console.error("❌ Error saving final audio file:", error);
      }
    }
    await session.disconnect();
    isRunning = false;
  }, 60000);
  process.on("SIGINT", async () => {
    console.log("\n👋 Interrupted by user. Closing session...");
    if (audioResponse && audioChunks.length > 0) {
      try {
        await saveWavFile(audioChunks, "gemini-response.wav");
      } catch (error) {
        console.error("❌ Error saving final audio file:", error);
      }
    }
    await session.disconnect();
    isRunning = false;
  });
  const waitForClose = () => {
    if (isRunning) {
      setTimeout(waitForClose, 1000);
    } else {
      process.exit(0);
    }
  };
  waitForClose();
 }
 main().catch((error) => {
  console.error("❌ Fatal error:", error);
  process.exit(1);
 });
@@ -1,17 +0,0 @@
 # Run LlamaIndex Server with simple steps
 1. Setup environment variables
 ```bash
 export OPENAI_API_KEY=<your-openai-api-key>
 ```
 2. Run the server
 ```bash
 npx tsx llamaindex-server/simple-workflow/index.ts
 ```
 3. Open the app at `http://localhost:4000` and start chatting with the agent
 ![Screenshot](./screenshot.png)
@@ -1,19 +0,0 @@
 import { OpenAI } from "@llamaindex/openai";
 import { LlamaIndexServer } from "@llamaindex/server";
 import { weather } from "@llamaindex/tools";
 import "dotenv/config";
 import { agent } from "llamaindex";
 const weatherAgent = agent({
  tools: [weather()],
  llm: new OpenAI({ model: "gpt-4o-mini" }),
 });
 new LlamaIndexServer({
  workflow: () => weatherAgent,
  uiConfig: {
    appTitle: "Weather Agent",
    starterQuestions: ["Ho Chi Minh city weather", "New York weather"],
  },
  port: 4000,
 }).start();
@@ -1,6 +1,8 @@
 import { mistral } from "@llamaindex/mistral";
 import { wiki } from "@llamaindex/tools";
-import { agent, tool } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const workflow = agent({
@@ -37,8 +37,10 @@ async function main() {
  const index = await VectorStoreIndex.init({
    storageContext,
  });
-  // topK for text is 0 and for image 1 => we only retrieve one image and no text based on the query
+  // topK for text is 0, for image 1, for audio 0  => we only retrieve one image and no text based on the query
-  const retriever = index.asRetriever({ topK: { TEXT: 0, IMAGE: 1 } });
+  const retriever = index.asRetriever({
    topK: { TEXT: 0, IMAGE: 1, AUDIO: 0 },
  });
  // NOTE: we set the contextRole to "user" (default is "system"). The reason is that GPT-4 does not support
  // images in a system message
  const chatEngine = new ContextChatEngine({ retriever, contextRole: "user" });
@@ -30,7 +30,7 @@ async function main() {
  const queryEngine = index.asQueryEngine({
    responseSynthesizer: getResponseSynthesizer("multi_modal"),
-    retriever: index.asRetriever({ topK: { TEXT: 3, IMAGE: 1 } }),
+    retriever: index.asRetriever({ topK: { TEXT: 3, IMAGE: 1, AUDIO: 0 } }),
  });
  const stream = await queryEngine.query({
    query: "Tell me more about Vincent van Gogh's famous paintings",
@@ -12,7 +12,9 @@ async function main() {
    nodes: [],
    storageContext,
  });
-  const retriever = index.asRetriever({ topK: { TEXT: 1, IMAGE: 3 } });
+  const retriever = index.asRetriever({
    topK: { TEXT: 1, IMAGE: 3, AUDIO: 0 },
  });
  const results = await retriever.retrieve({
    query: "what are Vincent van Gogh's famous paintings",
  });
@@ -1,32 +0,0 @@
 import { StartEvent, StopEvent, Workflow } from "llamaindex";
 type ContextData = {
  counter: number;
 };
 const contextData: ContextData = { counter: 0 };
 const workflow = new Workflow<ContextData, string, string>();
 workflow.addStep(
  {
    inputs: [StartEvent<string>],
  },
  async (context, startEvent) => {
    const input = startEvent.data;
    context.data.counter++;
    return new StopEvent(`Hello, ${input}!`);
  },
 );
 {
  const ret = await workflow.run("Alex", contextData);
  console.log(ret.data); // Hello, Alex!
 }
 {
  const ret = await workflow.run("World", contextData);
  console.log(ret.data); // Hello, World!
 }
 console.log(contextData.counter); // 2
@@ -26,7 +26,7 @@ import fs from "fs";
          },
          {
            type: "file",
-            data: fs.readFileSync("./data/manga.pdf"),
+            data: new Uint8Array(fs.readFileSync("./data/manga.pdf")),
            mimeType: "application/pdf",
          },
        ],
@@ -1,6 +1,7 @@
 import { openaiResponses } from "@llamaindex/openai";
 import { wiki } from "@llamaindex/tools";
-import { agent, tool } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { tool } from "llamaindex";
 import { z } from "zod";
 const workflow = agent({
@@ -21,7 +21,7 @@ async function main() {
          },
          {
            type: "file",
-            data: fs.readFileSync("./data/manga.pdf"),
+            data: Uint8Array.from(fs.readFileSync("./data/manga.pdf")),
            mimeType: "application/pdf",
          },
        ],
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/examples",
-  "version": "0.3.13",
+  "version": "0.3.14",
  "private": true,
  "scripts": {
    "lint": "eslint .",
@@ -11,60 +11,59 @@
    "@azure/cosmos": "^4.1.1",
    "@azure/identity": "^4.4.1",
    "@azure/search-documents": "^12.1.0",
-    "@llamaindex/anthropic": "^0.3.4",
+    "@llamaindex/anthropic": "^0.3.5",
-    "@llamaindex/astra": "^0.0.17",
+    "@llamaindex/astra": "^0.0.18",
-    "@llamaindex/azure": "^0.1.13",
+    "@llamaindex/azure": "^0.1.14",
-    "@llamaindex/chroma": "^0.0.17",
+    "@llamaindex/chroma": "^0.0.18",
-    "@llamaindex/clip": "^0.0.53",
+    "@llamaindex/clip": "^0.0.54",
-    "@llamaindex/cloud": "^4.0.4",
+    "@llamaindex/cloud": "^4.0.6",
-    "@llamaindex/cohere": "^0.0.17",
+    "@llamaindex/cohere": "^0.0.18",
-    "@llamaindex/core": "^0.6.3",
+    "@llamaindex/core": "^0.6.4",
-    "@llamaindex/deepinfra": "^0.0.53",
+    "@llamaindex/deepinfra": "^0.0.54",
-    "@llamaindex/env": "^0.1.29",
+    "@llamaindex/env": "^0.1.30",
-    "@llamaindex/firestore": "^1.0.10",
+    "@llamaindex/firestore": "^1.0.11",
-    "@llamaindex/google": "^0.2.5",
+    "@llamaindex/google": "^0.3.0",
-    "@llamaindex/groq": "^0.0.68",
+    "@llamaindex/groq": "^0.0.69",
-    "@llamaindex/huggingface": "^0.1.7",
+    "@llamaindex/huggingface": "^0.1.8",
-    "@llamaindex/milvus": "^0.1.12",
+    "@llamaindex/milvus": "^0.1.13",
-    "@llamaindex/mistral": "^0.1.3",
+    "@llamaindex/mistral": "^0.1.4",
-    "@llamaindex/mixedbread": "^0.0.17",
+    "@llamaindex/mixedbread": "^0.0.18",
-    "@llamaindex/mongodb": "^0.0.18",
+    "@llamaindex/mongodb": "^0.0.19",
-    "@llamaindex/elastic-search": "^0.1.3",
+    "@llamaindex/elastic-search": "^0.1.4",
-    "@llamaindex/node-parser": "^2.0.3",
+    "@llamaindex/node-parser": "^2.0.4",
-    "@llamaindex/ollama": "^0.1.3",
+    "@llamaindex/ollama": "^0.1.4",
-    "@llamaindex/openai": "^0.3.5",
+    "@llamaindex/openai": "^0.3.6",
-    "@llamaindex/pinecone": "^0.1.3",
+    "@llamaindex/pinecone": "^0.1.4",
-    "@llamaindex/portkey-ai": "^0.0.45",
+    "@llamaindex/portkey-ai": "^0.0.46",
-    "@llamaindex/postgres": "^0.0.46",
+    "@llamaindex/postgres": "^0.0.47",
-    "@llamaindex/qdrant": "^0.1.12",
+    "@llamaindex/qdrant": "^0.1.13",
-    "@llamaindex/readers": "^3.1.1",
+    "@llamaindex/readers": "^3.1.2",
-    "@llamaindex/replicate": "^0.0.45",
+    "@llamaindex/replicate": "^0.0.46",
-    "@llamaindex/upstash": "^0.0.17",
+    "@llamaindex/upstash": "^0.0.18",
-    "@llamaindex/vercel": "^0.1.3",
+    "@llamaindex/vercel": "^0.1.4",
-    "@llamaindex/vllm": "^0.0.39",
+    "@llamaindex/vllm": "^0.0.40",
-    "@llamaindex/voyage-ai": "^1.0.9",
+    "@llamaindex/voyage-ai": "^1.0.10",
-    "@llamaindex/weaviate": "^0.0.17",
+    "@llamaindex/weaviate": "^0.0.18",
-    "@llamaindex/workflow": "^1.0.4",
+    "@llamaindex/workflow": "^1.1.1",
-    "@llamaindex/deepseek": "^0.0.13",
+    "@llamaindex/deepseek": "^0.0.14",
-    "@llamaindex/fireworks": "^0.0.13",
+    "@llamaindex/fireworks": "^0.0.14",
-    "@llamaindex/together": "^0.0.13",
+    "@llamaindex/together": "^0.0.14",
-    "@llamaindex/jinaai": "^0.0.13",
+    "@llamaindex/jinaai": "^0.0.14",
-    "@llamaindex/perplexity": "^0.0.10",
+    "@llamaindex/perplexity": "^0.0.11",
-    "@llamaindex/server": "^0.1.6",
+    "@llamaindex/supabase": "^0.1.3",
-    "@llamaindex/supabase": "^0.1.2",
+    "@llamaindex/tools": "^0.0.9",
    "@llamaindex/tools": "^0.0.8",
    "@notionhq/client": "^2.2.15",
    "@pinecone-database/pinecone": "^4.0.0",
-    "@llamaindex/assemblyai": "^0.1.2",
+    "@llamaindex/assemblyai": "^0.1.3",
-    "@llamaindex/discord": "^0.1.2",
+    "@llamaindex/discord": "^0.1.3",
-    "@llamaindex/notion": "^0.1.2",
+    "@llamaindex/notion": "^0.1.3",
    "@vercel/postgres": "^0.10.0",
    "ai": "^4.0.0",
    "ajv": "^8.17.1",
    "commander": "^12.1.0",
    "dotenv": "^16.4.5",
    "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.10.3",
+    "llamaindex": "^0.10.5",
    "mongodb": "6.7.0",
    "postgres": "^3.4.4",
    "wikipedia": "^2.1.2",
@@ -15,9 +15,10 @@
    "circular-check": "madge --circular ./packages/**/**/dist/index.js",
    "release": "pnpm run build && changeset publish",
    "release-snapshot": "pnpm run build && changeset publish --tag snapshot",
-    "new-version": "changeset version && pnpm format:write && pnpm run build",
+    "new-version": "changeset version && pnpm postversion && pnpm format:write && pnpm run build",
    "new-snapshot": "pnpm run build && changeset version --snapshot",
-    "lint-staged": "lint-staged"
+    "lint-staged": "lint-staged",
    "postversion": "node scripts/repin-workflow.mjs"
  },
  "devDependencies": {
    "@changesets/cli": "^2.27.5",
@@ -1,5 +1,20 @@
 # @llamaindex/autotool
 ## 7.0.5
 ### Patch Changes
 - llamaindex@0.10.5
 ## 7.0.4
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
 ## 7.0.3
 ### Patch Changes
@@ -1,5 +1,22 @@
 # @llamaindex/autotool-01-node-example
 ## 0.0.106
 ### Patch Changes
 - llamaindex@0.10.5
 - @llamaindex/autotool@7.0.5
 ## 0.0.105
 ### Patch Changes
 - Updated dependencies [2225ffd]
 - Updated dependencies [6ddf1c1]
 - Updated dependencies [41953a3]
  - llamaindex@0.10.4
  - @llamaindex/autotool@7.0.4
 ## 0.0.104
 ### Patch Changes
@@ -13,5 +13,5 @@
  "scripts": {
    "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
  },
-  "version": "0.0.104"
+  "version": "0.0.106"
 }
@@ -6,7 +6,7 @@
    "url": "git+https://github.com/run-llama/LlamaIndexTS.git",
    "directory": "packages/autotool"
  },
-  "version": "7.0.3",
+  "version": "7.0.5",
  "description": "auto transpile your JS function to LLM Agent compatible",
  "files": [
    "dist",
@@ -1,5 +1,19 @@
 # @llamaindex/cloud
 ## 4.0.6
 ### Patch Changes
 - Updated dependencies [9b2e25a]
  - @llamaindex/core@0.6.4
  - @llamaindex/env@0.1.30
 ## 4.0.5
 ### Patch Changes
 - 2225ffd: feat: bump llama cloud sdk
 ## 4.0.4
 ### Patch Changes
@@ -2,15 +2,6 @@
 > LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
 ## Usage
 ```ts
 import { OpenAPI } from "@llamaindex/cloud/api";
 OpenAPI.TOKEN = "YOUR_API_KEY";
 OpenAPI.BASE = "https://api.cloud.llamaindex.ai/";
 // ...
 ```
 For more information, see the [API documentation](https://docs.cloud.llamaindex.ai/).
 ## License
@@ -1,22 +1,19 @@
-import { defineConfig } from "@hey-api/openapi-ts";
+import { defaultPlugins, defineConfig } from "@hey-api/openapi-ts";
 export default defineConfig({
  // you can download this file to get the latest version of the OpenAPI document
  // @link https://api.cloud.llamaindex.ai/api/openapi.json
  input: "./openapi.json",
  client: "@hey-api/client-fetch",
  output: {
    path: "./src/client",
    format: "prettier",
    lint: "eslint",
  },
  plugins: [
-    "@hey-api/schemas",
+    ...defaultPlugins,
-    "@hey-api/sdk",
+    "@hey-api/client-fetch",
    {
-      enums: "javascript",
+      name: "@hey-api/sdk",
      identifierCase: "preserve",
      name: "@hey-api/typescript",
    },
  ],
 });
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloud",
-  "version": "4.0.4",
+  "version": "4.0.6",
  "type": "module",
  "license": "MIT",
  "scripts": {
@@ -55,8 +55,8 @@
    "directory": "packages/cloud"
  },
  "devDependencies": {
-    "@hey-api/client-fetch": "^0.6.0",
+    "@hey-api/client-fetch": "^0.10.0",
-    "@hey-api/openapi-ts": "^0.61.0",
+    "@hey-api/openapi-ts": "^0.66.7",
    "@llamaindex/core": "workspace:*",
    "@llamaindex/env": "workspace:*"
  },
@@ -1 +1,11 @@
 import { client } from "./client/client.gen";
 client.setConfig({
  baseUrl: "https://api.cloud.llamaindex.ai/",
  headers: {
    "X-SDK-Name": "llamaindex-ts",
  },
 });
 export * from "./client";
 export { client };
@@ -4,7 +4,8 @@ import { Document, FileReader } from "@llamaindex/core/schema";
 import { fs, getEnv, path } from "@llamaindex/env";
 import pRetry from "p-retry";
 import {
-  type Body_upload_file_api_v1_parsing_upload_post,
+  type BodyUploadFileApiParsingUploadPost,
  type FailPageMode,
  type ParserLanguages,
  type ParsingMode,
  getJobApiV1ParsingJobJobIdGet,
@@ -162,6 +163,15 @@ export class LlamaParseReader extends FileReader {
  content_guideline_instruction?: string | undefined;
  adaptive_long_table?: boolean | undefined;
  model?: string | undefined;
  auto_mode_configuration_json?: string | undefined;
  compact_markdown_table?: boolean | undefined;
  markdown_table_multiline_header_separator?: string | undefined;
  page_error_tolerance?: number | undefined;
  replace_failed_page_mode?: FailPageMode | undefined;
  replace_failed_page_with_error_message_prefix?: string | undefined;
  replace_failed_page_with_error_message_suffix?: string | undefined;
  save_images?: boolean | undefined;
  preset?: string | undefined;
  constructor(
    params: Partial<Omit<LlamaParseReader, "language" | "apiKey">> & {
@@ -331,11 +341,23 @@ export class LlamaParseReader extends FileReader {
      content_guideline_instruction: this.content_guideline_instruction,
      adaptive_long_table: this.adaptive_long_table,
      model: this.model,
      auto_mode_configuration_json: this.auto_mode_configuration_json,
      compact_markdown_table: this.compact_markdown_table,
      markdown_table_multiline_header_separator:
        this.markdown_table_multiline_header_separator,
      page_error_tolerance: this.page_error_tolerance,
      replace_failed_page_mode: this.replace_failed_page_mode,
      replace_failed_page_with_error_message_prefix:
        this.replace_failed_page_with_error_message_prefix,
      replace_failed_page_with_error_message_suffix:
        this.replace_failed_page_with_error_message_suffix,
      save_images: this.save_images,
      preset: this.preset,
    } satisfies {
-      [Key in keyof Body_upload_file_api_v1_parsing_upload_post]-?:
+      [Key in keyof BodyUploadFileApiParsingUploadPost]-?:
-        | Body_upload_file_api_v1_parsing_upload_post[Key]
+        | BodyUploadFileApiParsingUploadPost[Key]
        | undefined;
-    } as unknown as Body_upload_file_api_v1_parsing_upload_post;
+    } as unknown as BodyUploadFileApiParsingUploadPost;
    const response = await uploadFileApiV1ParsingUploadPost({
      client: this.#client,
@@ -382,10 +404,6 @@ export class LlamaParseReader extends FileReader {
              client: this.#client,
              throwOnError: true,
              path: { job_id: jobId },
              query: {
                project_id: this.project_id ?? null,
                organization_id: this.organization_id ?? null,
              },
              signal: AbortSignal.timeout(this.maxTimeout * 1000),
            }),
          {
@@ -431,7 +449,6 @@ export class LlamaParseReader extends FileReader {
                throwOnError: true,
                path: { job_id: jobId },
                query: {
                  project_id: this.project_id ?? null,
                  organization_id: this.organization_id ?? null,
                },
                signal: AbortSignal.timeout(this.maxTimeout * 1000),
@@ -445,7 +462,6 @@ export class LlamaParseReader extends FileReader {
                throwOnError: true,
                path: { job_id: jobId },
                query: {
                  project_id: this.project_id ?? null,
                  organization_id: this.organization_id ?? null,
                },
                signal: AbortSignal.timeout(this.maxTimeout * 1000),
@@ -459,7 +475,6 @@ export class LlamaParseReader extends FileReader {
                throwOnError: true,
                path: { job_id: jobId },
                query: {
                  project_id: this.project_id ?? null,
                  organization_id: this.organization_id ?? null,
                },
                signal: AbortSignal.timeout(this.maxTimeout * 1000),
@@ -689,10 +704,6 @@ export class LlamaParseReader extends FileReader {
          job_id: jobId,
          name: imageName,
        },
        query: {
          project_id: this.project_id ?? null,
          organization_id: this.organization_id ?? null,
        },
      });
    if (response.error) {
      throw new Error(`Failed to download image: ${response.error.detail}`);
@@ -1,5 +1,13 @@
 # @llamaindex/community
 ## 0.0.98
 ### Patch Changes
 - Updated dependencies [9b2e25a]
  - @llamaindex/core@0.6.4
  - @llamaindex/env@0.1.30
 ## 0.0.97
 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/community",
  "description": "Community package for LlamaIndexTS",
-  "version": "0.0.97",
+  "version": "0.0.98",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
@@ -1,5 +1,13 @@
 # @llamaindex/core
 ## 0.6.4
 ### Patch Changes
 - 9b2e25a: Use Uint8Array instead of Buffer for file type messages (works with non-NodeJS)
 - Updated dependencies [9b2e25a]
  - @llamaindex/env@0.1.30
 ## 0.6.3
 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/core",
  "type": "module",
-  "version": "0.6.3",
+  "version": "0.6.4",
  "description": "LlamaIndex Core Module",
  "exports": {
    "./agent": {
@@ -1,4 +1,5 @@
 export { BaseLLM, ToolCallLLM } from "./base";
 export { LiveLLM, LiveLLMSession, liveEvents, type LiveEvent } from "./live";
 export type {
  BaseTool,
  BaseToolWithCall,
@@ -15,11 +16,15 @@ export type {
  LLMCompletionParamsNonStreaming,
  LLMCompletionParamsStreaming,
  LLMMetadata,
  LiveConnectConfig,
  MessageContent,
  MessageContentAudioDetail,
  MessageContentDetail,
  MessageContentFileDetail,
  MessageContentImageDataDetail,
  MessageContentImageDetail,
  MessageContentTextDetail,
  MessageContentVideoDetail,
  MessageType,
  PartialToolCall,
  TextChatMessage,
@@ -0,0 +1,87 @@
 import type {
  ChatMessage,
  LiveConnectConfig,
  MessageContentAudioDetail,
  MessageContentTextDetail,
 } from "./type";
 export type OpenEvent = { type: "open" };
 export type AudioEvent = MessageContentAudioDetail;
 export type TextEvent = MessageContentTextDetail;
 export type ErrorEvent = { type: "error"; error: unknown };
 export type CloseEvent = { type: "close" };
 export type SetupCompleteEvent = { type: "setupComplete" };
 export type LiveEvent =
  | OpenEvent
  | AudioEvent
  | TextEvent
  | ErrorEvent
  | CloseEvent
  | SetupCompleteEvent;
 export const liveEvents = {
  open: { include: (e: LiveEvent): e is OpenEvent => e.type === "open" },
  audio: {
    include: (e: LiveEvent): e is AudioEvent => e.type === "audio",
  },
  text: { include: (e: LiveEvent): e is TextEvent => e.type === "text" },
  error: {
    include: (e: LiveEvent): e is ErrorEvent => e.type === "error",
  },
  close: {
    include: (e: LiveEvent): e is CloseEvent => e.type === "close",
  },
  setupComplete: {
    include: (e: LiveEvent): e is SetupCompleteEvent =>
      e.type === "setupComplete",
  },
 };
 export abstract class LiveLLMSession {
  protected eventQueue: LiveEvent[] = [];
  protected eventResolvers: ((value: LiveEvent) => void)[] = [];
  protected closed = false;
  abstract sendMessage(message: ChatMessage): void;
  async *streamEvents(): AsyncIterable<LiveEvent> {
    while (true) {
      const event = await this.nextEvent();
      if (event === undefined) {
        break;
      }
      yield event;
    }
  }
  abstract disconnect(): Promise<void>;
  protected async nextEvent(): Promise<LiveEvent | undefined> {
    if (this.eventQueue.length) {
      return Promise.resolve(this.eventQueue.shift());
    }
    return new Promise((resolve) => {
      this.eventResolvers.push(resolve);
    });
  }
  //Uses an async queue to send events to the client
  // if the consumer is waiting for an event, it will be resolved immediately
  // otherwise, the event will be queued up and sent when the consumer is ready
  pushEventToQueue(event: LiveEvent) {
    if (this.eventResolvers.length) {
      //resolving the promise with the event
      this.eventResolvers.shift()!(event);
    } else {
      this.eventQueue.push(event);
    }
  }
 }
 export abstract class LiveLLM {
  abstract connect(config?: LiveConnectConfig): Promise<LiveLLMSession>;
 }
@@ -2,6 +2,7 @@ import type { Tokenizers } from "@llamaindex/env/tokenizers";
 import type { JSONSchemaType } from "ajv";
 import { z } from "zod";
 import type { JSONObject, JSONValue } from "../global";
 import type { ModalityType } from "../schema";
 /**
 * @internal
 */
@@ -163,15 +164,39 @@ export type MessageContentImageDetail = {
  detail?: "high" | "low" | "auto";
 };
 export type MessageContentAudioDetail = {
  type: "audio";
  //audio could be a base64 string as well
  data: string | Uint8Array;
  mimeType: string;
 };
 export type MessageContentVideoDetail = {
  type: "video";
  //video could be a base64 string as well
  data: string | Uint8Array;
  mimeType: string;
 };
 export type MessageContentImageDataDetail = {
  type: "image";
  //image could be a base64 string as well
  data: string | Uint8Array;
  mimeType: string;
 };
 export type MessageContentFileDetail = {
  type: "file";
-  data: Buffer;
+  data: Uint8Array;
  mimeType: string;
 };
 export type MessageContentDetail =
  | MessageContentTextDetail
  | MessageContentImageDetail
  | MessageContentAudioDetail
  | MessageContentVideoDetail
  | MessageContentImageDataDetail
  | MessageContentFileDetail;
 /**
@@ -267,3 +292,9 @@ export type ToolOutput = {
  output: JSONValue;
  isError: boolean;
 };
 export interface LiveConnectConfig {
  tools?: BaseTool[];
  responseModality?: ModalityType[];
  systemInstruction?: string;
 }
@@ -431,6 +431,7 @@ export interface NodeWithScore<T extends Metadata = Metadata> {
 export enum ModalityType {
  TEXT = "TEXT",
  IMAGE = "IMAGE",
  AUDIO = "AUDIO",
 }
 type NodesByType = {
@@ -1,5 +1,11 @@
 # @llamaindex/env
 ## 0.1.30
 ### Patch Changes
 - 9b2e25a: Use Uint8Array instead of Buffer for file type messages (works with non-NodeJS)
 ## 0.1.29
 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/env",
  "description": "environment wrapper, supports all JS environment including node, deno, bun, edge runtime, and cloudflare worker",
-  "version": "0.1.29",
+  "version": "0.1.30",
  "type": "module",
  "types": "dist/index.d.ts",
  "module": "dist/index.js",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions[bot]	4a18a2eb3d	Release 0.10.5 (#1922 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-05-09 14:30:39 +07:00
ANKIT VARSHNEY	206b491724	feat: Support for google live api (#1905 )	2025-05-09 14:20:40 +07:00
Marcus Schiesser	9b2e25a184	fix: Use Uint8Array instead of Buffer for file type messages (works w… (#1921 )	2025-05-08 13:19:59 +07:00
github-actions[bot]	b29521bf6c	Release @llamaindex/google@0.2.6 (#1918 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-05-07 16:31:58 +07:00
Marcus Schiesser	73e25787e7	feat: add gemini-2.5-pro-preview-05-06 (#1917 )	2025-05-07 16:18:21 +07:00
Marcus Schiesser	3ce80540fe	docs: add workflows documentation and update installation instruction… (#1916 )	2025-05-07 15:22:08 +07:00
Marcus Schiesser	dbc1ee3089	docs: update installation instructions for LlamaIndex to include Work… (#1915 )	2025-05-07 12:31:48 +07:00
github-actions[bot]	3b45191228	Release 0.10.4 (#1901 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-05-07 11:11:11 +07:00
Marcus Schiesser	aaf2f8b2db	docs: fix docs for agents (#1914 )	2025-05-07 11:03:08 +07:00
Marcus Schiesser	6ddf1c1b1f	chore: fixes for workflows before release (#1908 )	2025-05-07 09:29:09 +07:00
Marcus Schiesser	a8717d5ece	chore: ensure pinning workflow version (#1907 )	2025-05-06 12:51:13 +07:00
Huu Le	7e8e4549f2	chore: update @llama-flow/core to version 0.4.1 and export stream api (#1906 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-05-05 16:06:44 +07:00
Alex Yang	cc3fe92a22	docs: update llama-flow	2025-05-04 02:28:04 -07:00
Alex Yang	63ab0dba4e	chore: drop node.js 18 support (#1904 )	2025-05-02 11:51:18 -07:00
Alex Yang	2225ffd1d4	feat: bump llama cloud sdk (#1903 )	2025-05-01 13:30:52 -07:00
Marcus Schiesser	bc5334249b	chore: migrate agentworkflows to llama-flow (#1895 ) Co-authored-by: leehuwuj <leehuwuj@gmail.com>	2025-04-30 18:14:17 +07:00
Thuc Pham	41953a3ef9	fix: node10 module resolution fail in sub llamaindex packages (#1900 )	2025-04-29 17:47:50 +07:00