@aigne/example-workflow-concurrency

Workflow Concurrency Demo

This is a demonstration of using AIGNE Framework to build a concurrency workflow. The example now supports both one-shot and interactive chat modes, along with customizable model settings and pipeline input/output.

flowchart LR
in(In)
out(Out)
featureExtractor(Feature Extractor)
audienceAnalyzer(Audience Analyzer)
aggregator(Aggregator)

in --> featureExtractor --> aggregator
in --> audienceAnalyzer --> aggregator
aggregator --> out

classDef inputOutput fill:#f9f0ed,stroke:#debbae,stroke-width:2px,color:#b35b39,font-weight:bolder;
classDef processing fill:#F0F4EB,stroke:#C2D7A7,stroke-width:2px,color:#6B8F3C,font-weight:bolder;

class in inputOutput
class out inputOutput
class featureExtractor processing
class audienceAnalyzer processing
class aggregator processing

Prerequisites

• Node.js (>=20.0) and npm installed on your machine
• An OpenAI API key for interacting with OpenAI's services
• Optional dependencies (if running the example from source code):
  • Bun for running unit tests & examples
  • Pnpm for package management

Quick Start (No Installation Required)

export OPENAI_API_KEY=YOUR_OPENAI_API_KEY # Set your OpenAI API key

# Run in one-shot mode (default)
npx -y @aigne/example-workflow-concurrency

# Run in interactive chat mode
npx -y @aigne/example-workflow-concurrency --chat

# Use pipeline input
echo "Analyze product: Smart home assistant with voice control and AI learning capabilities" | npx -y @aigne/example-workflow-concurrency

Installation

Clone the Repository

git clone https://github.com/AIGNE-io/aigne-framework

Install Dependencies

cd aigne-framework/examples/workflow-concurrency

pnpm install

Setup Environment Variables

Setup your OpenAI API key in the .env.local file:

OPENAI_API_KEY="" # Set your OpenAI API key here

Using Different Models

You can use different AI models by setting the MODEL environment variable along with the corresponding API key. The framework supports multiple providers:

• OpenAI: MODEL="openai:gpt-4.1" with OPENAI_API_KEY
• Anthropic: MODEL="anthropic:claude-3-7-sonnet-latest" with ANTHROPIC_API_KEY
• Google Gemini: MODEL="gemini:gemini-2.0-flash" with GEMINI_API_KEY
• AWS Bedrock: MODEL="bedrock:us.amazon.nova-premier-v1:0" with AWS credentials
• DeepSeek: MODEL="deepseek:deepseek-chat" with DEEPSEEK_API_KEY
• OpenRouter: MODEL="openrouter:openai/gpt-4o" with OPEN_ROUTER_API_KEY
• xAI: MODEL="xai:grok-2-latest" with XAI_API_KEY
• Ollama: MODEL="ollama:llama3.2" with OLLAMA_DEFAULT_BASE_URL

For detailed configuration examples, please refer to the .env.local.example file in this directory.

Run the Example

pnpm start # Run in one-shot mode (default)

# Run in interactive chat mode
pnpm start -- --chat

# Use pipeline input
echo "Analyze product: Smart home assistant with voice control and AI learning capabilities" | pnpm start

Run Options

The example supports the following command-line parameters:

Parameter	Description	Default
--chat	Run in interactive chat mode	Disabled (one-shot mode)
--model <provider[:model]>	AI model to use in format 'provider[:model]' where model is optional. Examples: 'openai' or 'openai:gpt-4o-mini'	openai
--temperature <value>	Temperature for model generation	Provider default
--top-p <value>	Top-p sampling value	Provider default
--presence-penalty <value>	Presence penalty value	Provider default
--frequency-penalty <value>	Frequency penalty value	Provider default
--log-level <level>	Set logging level (ERROR, WARN, INFO, DEBUG, TRACE)	INFO
--input, -i <input>	Specify input directly	None

Examples

# Run in chat mode (interactive)
pnpm start -- --chat

# Set logging level
pnpm start -- --log-level DEBUG

# Use pipeline input
echo "Analyze product: Smart home assistant with voice control and AI learning capabilities" | pnpm start

Example

The following example demonstrates how to build a concurrency workflow:

import { AIAgent, AIGNE, ProcessMode, TeamAgent } from "@aigne/core";
import { OpenAIChatModel } from "@aigne/core/models/openai-chat-model.js";

const { OPENAI_API_KEY } = process.env;

const model = new OpenAIChatModel({
  apiKey: OPENAI_API_KEY,
});

const featureExtractor = AIAgent.from({
  instructions: `\
You are a product analyst. Extract and summarize the key features of the product.

Product description:
{{product}}`,
  outputKey: "features",
});

const audienceAnalyzer = AIAgent.from({
  instructions: `\
You are a market researcher. Identify the target audience for the product.

Product description:
{{product}}`,
  outputKey: "audience",
});

const aigne = new AIGNE({ model });

// 创建一个 TeamAgent 来处理并行工作流
const teamAgent = TeamAgent.from({
  skills: [featureExtractor, audienceAnalyzer],
  mode: ProcessMode.parallel,
});

const result = await aigne.invoke(teamAgent, {
  product: "AIGNE is a No-code Generative AI Apps Engine",
});

console.log(result);

// Output:
// {
//   features: "**Product Name:** AIGNE\n\n**Product Type:** No-code Generative AI Apps Engine\n\n...",
//   audience: "**Small to Medium Enterprises (SMEs)**: \n   - Businesses that may not have extensive IT resources or budget for app development but are looking to leverage AI to enhance their operations or customer engagement.\n\n...",
// }

License

This project is licensed under the MIT License.