feat: add asQueryTool to index and add factory methods for simplifying agent usage (#1715)

Author: marcusschiesser

.changeset/sour-rats-complain.md

@@ -0,0 +1,7 @@
+---
+"llamaindex": patch
+"@llamaindex/workflow": patch
+"@llamaindex/core": patch
+---
+
+Add factory methods agent and multiAgent to simplify agent usage

.changeset/wise-ghosts-play.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+feat: add asQueryTool to index

apps/next/src/app/(home)/page.tsx

@@ -125,19 +125,20 @@ const response = await agent.chat({
           description="Truly powerful retrieval-augmented generation applications use agentic techniques, and LlamaIndex.TS makes it easy to build them."
         >
           <CodeBlock
-            code={`import { FunctionTool } from "llamaindex";
-import { OpenAIAgent } from "@llamaindex/openai";
+            code={`import { agent } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
 
-const interpreterTool = FunctionTool.from(...);
-const systemPrompt = \`...\`;
+// using a previously created LlamaIndex index to query information from
+const queryTool = index.queryTool();
 
-const agent = new OpenAIAgent({
-  llm,
-  tools: [interpreterTool],
-  systemPrompt,
+const agent = agent({
+  llm: new OpenAI({
+    model: "gpt-4o",
+  }),
+  tools: [queryTool],
 });
 
-await agent.chat('...');`}
+await agent.run('...');`}
             lang="ts"
           />
         </Feature>

apps/next/src/content/docs/llamaindex/modules/agent_workflow.mdx

@@ -6,25 +6,7 @@ import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
 import CodeSource from "!raw-loader!../../../../../../../examples/agentworkflow/blog_writer.ts";
 import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 
-`AgentWorkflow` is a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base `Workflow` system and provides a streamlined interface for agent interactions.
-
-## Installation
-
-You'll need to install the `@llamaindex/workflow` package:
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install @llamaindex/workflow
-	```
-
-	```shell tab="yarn"
-	yarn add @llamaindex/workflow
-	```
-
-	```shell tab="pnpm"
-	pnpm add @llamaindex/workflow
-	```
-</Tabs>
+Agent Workflows are a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base `Workflow` system and provides a streamlined interface for agent interactions.
 
 ## Usage
 
@@ -33,7 +15,7 @@ You'll need to install the `@llamaindex/workflow` package:
 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 { AgentWorkflow, FunctionTool } from "llamaindex";
+import { agent, FunctionTool } from "llamaindex";
 import { OpenAI } from "@llamaindex/openai";
 
 // Define a joke-telling tool
@@ -45,8 +27,8 @@ const jokeTool = FunctionTool.from(
   }
 );
 
-// Create an agent workflow with the tool
-const workflow = AgentWorkflow.fromTools({
+// Create an single agent workflow with the tool
+const workflow = agent({
   tools: [jokeTool],
   llm: new OpenAI({
     model: "gpt-4o-mini",
@@ -60,7 +42,7 @@ console.log(result); // Baby Llama is called cria
 
 ### Event Streaming
 
-`AgentWorkflow` provides a unified interface for event streaming, making it easy to track and respond to different events during execution:
+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";
@@ -81,7 +63,7 @@ for await (const event of context) {
 
 ### Multi-Agent Workflow
 
-`AgentWorkflow` can orchestrate multiple agents, enabling complex interactions and task handoffs. Each agent in a multi-agent workflow requires:
+An Agent Workflow can orchestrate multiple agents, enabling complex interactions and task handoffs. Each agent in a multi-agent workflow requires:
 
 - `name`: Unique identifier for the agent
 - `description`: Purpose description used for task routing
@@ -91,12 +73,12 @@ for await (const event of context) {
 Here's an example of a multi-agent system that combines joke-telling and weather information:
 
 ```typescript
-import { AgentWorkflow, FunctionAgent, FunctionTool } from "llamaindex";
+import { multiAgent, agent, FunctionTool } from "llamaindex";
 import { OpenAI } from "@llamaindex/openai";
 import { z } from "zod";
 
 // Create a weather agent
-const weatherAgent = new FunctionAgent({
+const weatherAgent = agent({
   name: "WeatherAgent",
   description: "Provides weather information for any city",
   tools: [
@@ -115,7 +97,7 @@ const weatherAgent = new FunctionAgent({
 });
 
 // Create a joke-telling agent
-const jokeAgent = new FunctionAgent({
+const jokeAgent = agent({
   name: "JokeAgent",
   description: "Tells jokes and funny stories",
   tools: [jokeTool], // Using the joke tool defined earlier
@@ -124,7 +106,7 @@ const jokeAgent = new FunctionAgent({
 });
 
 // Create the multi-agent workflow
-const workflow = new AgentWorkflow({
+const workflow = multiAgent({
   agents: [jokeAgent, weatherAgent],
   rootAgent: jokeAgent, // Start with the joke agent
 });

e2e/examples/nextjs-node-runtime/src/actions/openai.ts

@@ -1,13 +1,7 @@
 "use server";
 import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-import {
-  OpenAI,
-  OpenAIAgent,
-  QueryEngineTool,
-  Settings,
-  VectorStoreIndex,
-} from "llamaindex";
+import { OpenAI, OpenAIAgent, Settings, VectorStoreIndex } from "llamaindex";
 
 Settings.llm = new OpenAI({
   apiKey: process.env.NEXT_PUBLIC_OPENAI_KEY ?? "FAKE_KEY_TO_PASS_TESTS",
@@ -31,23 +25,20 @@ export async function getOpenAIModelRequest(query: string) {
     const reader = new SimpleDirectoryReader();
     const documents = await reader.loadData(currentDir);
     const index = await VectorStoreIndex.fromDocuments(documents);
-    const retriever = index.asRetriever({
-      similarityTopK: 10,
-    });
-    const queryEngine = index.asQueryEngine({
-      retriever,
-    });
 
     // define the query engine as a tool
     const tools = [
-      new QueryEngineTool({
-        queryEngine: queryEngine,
+      index.queryTool({
+        options: {
+          similarityTopK: 10,
+        },
         metadata: {
           name: "deployment_details_per_env",
           description: `This tool can answer detailed questions about deployments happened in various environments.`,
         },
       }),
     ];
+
     // create the agent
     const agent = new OpenAIAgent({ tools });
 

examples/agent/large_toolcall.ts

@@ -1,5 +1,5 @@
 import { OpenAI } from "@llamaindex/openai";
-import { AgentWorkflow, FunctionTool } from "llamaindex";
+import { FunctionTool, agent } from "llamaindex";
 import { z } from "zod";
 
 const csvData =
@@ -33,7 +33,7 @@ const userQuestion = "which are the best comedies after 2010?";
   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.";
 
-  const workflow = AgentWorkflow.fromTools({
+  const workflow = agent({
     tools: [interpreterTool],
     llm,
     verbose: false,

examples/agent/openai.ts

@@ -1,5 +1,5 @@
 import { OpenAI } from "@llamaindex/openai";
-import { AgentWorkflow, FunctionTool } from "llamaindex";
+import { FunctionTool, agent } from "llamaindex";
 import { z } from "zod";
 
 const sumNumbers = FunctionTool.from(
@@ -27,7 +27,7 @@ const divideNumbers = FunctionTool.from(
 );
 
 async function main() {
-  const workflow = AgentWorkflow.fromTools({
+  const workflow = agent({
     tools: [sumNumbers, divideNumbers],
     llm: new OpenAI({ model: "gpt-4o-mini" }),
     verbose: false,

examples/agent/wiki.ts

@@ -1,12 +1,12 @@
 import { OpenAI } from "@llamaindex/openai";
-import { AgentStream, AgentWorkflow } from "llamaindex";
+import { AgentStream, agent } from "llamaindex";
 import { WikipediaTool } from "../wiki";
 
 async function main() {
   const llm = new OpenAI({ model: "gpt-4-turbo" });
   const wikiTool = new WikipediaTool();
 
-  const workflow = AgentWorkflow.fromTools({
+  const workflow = agent({
     tools: [wikiTool],
     llm,
     verbose: false,

examples/agentworkflow/blog_writer.ts renamed to examples/agentworkflow/blog-writer.ts

@@ -1,11 +1,11 @@
 import { OpenAI } from "@llamaindex/openai";
 import fs from "fs";
 import {
+  agent,
   AgentToolCall,
   AgentToolCallResult,
-  AgentWorkflow,
-  FunctionAgent,
   FunctionTool,
+  multiAgent,
 } from "llamaindex";
 import os from "os";
 import { z } from "zod";
@@ -34,7 +34,7 @@ const saveFileTool = FunctionTool.from(
 );
 
 async function main() {
-  const reportAgent = new FunctionAgent({
+  const reportAgent = agent({
     name: "ReportAgent",
     description:
       "Responsible for crafting well-written blog posts based on research findings",
@@ -43,7 +43,7 @@ async function main() {
     llm,
   });
 
-  const researchAgent = new FunctionAgent({
+  const researchAgent = agent({
     name: "ResearchAgent",
     description:
       "Responsible for gathering relevant information from the internet",
@@ -53,7 +53,7 @@ async function main() {
     llm,
   });
 
-  const workflow = new AgentWorkflow({
+  const workflow = multiAgent({
     agents: [researchAgent, reportAgent],
     rootAgent: researchAgent,
   });

examples/agentworkflow/multiple_agents.ts renamed to examples/agentworkflow/multiple-agents.ts

@@ -5,14 +5,14 @@
  */
 import { OpenAI } from "@llamaindex/openai";
 import {
+  agent,
   AgentInput,
   AgentOutput,
   AgentStream,
   AgentToolCall,
   AgentToolCallResult,
-  AgentWorkflow,
-  FunctionAgent,
   FunctionTool,
+  multiAgent,
   StopEvent,
 } from "llamaindex";
 import { z } from "zod";
@@ -55,15 +55,15 @@ const temperatureFetcherTool = FunctionTool.from(
 
 // Create agents
 async function multiWeatherAgent() {
-  const converterAgent = new FunctionAgent({
+  const converterAgent = agent({
     name: "TemperatureConverterAgent",
     description:
       "An agent that can convert temperatures from Fahrenheit to Celsius.",
     tools: [temperatureConverterTool],
     llm,
   });
 
-  const weatherAgent = new FunctionAgent({
+  const weatherAgent = agent({
     name: "FetchWeatherAgent",
     description: "An agent that can get the weather in a city. ",
     systemPrompt:
@@ -76,7 +76,7 @@ async function multiWeatherAgent() {
   });
 
   // Create agent workflow with the agents
-  const workflow = new AgentWorkflow({
+  const workflow = multiAgent({
     agents: [weatherAgent, converterAgent],
     rootAgent: weatherAgent,
     verbose: false,

examples/agentworkflow/single_agent.ts renamed to examples/agentworkflow/single-agent.ts

@@ -2,17 +2,15 @@
  * This example shows how to use AgentWorkflow as a single agent with tools
  */
 import { OpenAI } from "@llamaindex/openai";
-import { AgentWorkflow, Settings } from "llamaindex";
+import { Settings, agent } from "llamaindex";
 import { getWeatherTool } from "../agent/utils/tools";
 
-const llm = new OpenAI({
+Settings.llm = new OpenAI({
   model: "gpt-4o",
 });
 
-Settings.llm = llm;
-
 async function singleWeatherAgent() {
-  const workflow = AgentWorkflow.fromTools({
+  const workflow = agent({
     tools: [getWeatherTool],
     verbose: false,
   });

examples/agentworkflow/with-anthropic.ts

@@ -1,10 +1,10 @@
 import fs from "fs";
 import {
+  agent,
   AgentToolCall,
   AgentToolCallResult,
-  AgentWorkflow,
-  FunctionAgent,
   FunctionTool,
+  multiAgent,
 } from "llamaindex";
 import { z } from "zod";
 
@@ -63,7 +63,7 @@ const saveFileTool = FunctionTool.from(
 );
 
 async function main() {
-  const reportAgent = new FunctionAgent({
+  const reportAgent = agent({
     name: "ReportAgent",
     description:
       "Responsible for creating concise reports about weather and inflation data",
@@ -72,7 +72,7 @@ async function main() {
     llm,
   });
 
-  const researchAgent = new FunctionAgent({
+  const researchAgent = agent({
     name: "ResearchAgent",
     description:
       "Responsible for gathering relevant information from the internet",
@@ -82,7 +82,7 @@ async function main() {
     llm,
   });
 
-  const workflow = new AgentWorkflow({
+  const workflow = multiAgent({
     agents: [researchAgent, reportAgent],
     rootAgent: researchAgent,
   });

packages/core/src/utils/index.ts

@@ -80,8 +80,9 @@ export {
   extractText,
   imageToDataUrl,
   messagesToHistory,
-  MockLLM,
   toToolDescriptions,
 } from "./llms";
 
+export { MockLLM } from "./mock";
+
 export { objectEntries } from "./object-entries";