Workflow Orchestration

direction: down

User: {
  shape: c4-person
}

OrchestratorAgent: {
  label: "Orchestrator Agent"
  shape: rectangle

  finder: {
    label: "Finder Agent"
    shape: rectangle
  }

  writer: {
    label: "Writer Agent"
    shape: rectangle
  }
}

Skills: {
  label: "Skills / Tools"
  shape: rectangle
  style: {
    stroke-dash: 2
  }

  puppeteer: {
    label: "Puppeteer\n(Web Scraping)"
    shape: rectangle
  }

  filesystem: {
    label: "Filesystem\n(Read/Write)"
    shape: cylinder
  }
}

User -> OrchestratorAgent: "1. Submits research task"
OrchestratorAgent -> OrchestratorAgent.finder: "2. Delegate: Find info"
OrchestratorAgent.finder -> Skills.puppeteer: "3. Scrape web"
OrchestratorAgent.finder -> Skills.filesystem: "4. Save findings"
OrchestratorAgent -> OrchestratorAgent.writer: "5. Delegate: Compile report"
OrchestratorAgent.writer -> Skills.filesystem: "6. Write final report"

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

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

# Use pipeline input
echo "Research ArcBlock and compile a report about their products and architecture" | npx -y @aigne/example-workflow-orchestrator

aigne observe

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

cd aigne-framework/examples/workflow-orchestrator
pnpm install

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

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

# Use pipeline input
echo "Research ArcBlock and compile a report about their products and architecture" | pnpm start

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

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

import { OrchestratorAgent } from "@aigne/agent-library/orchestrator/index.js";
import { AIAgent, AIGNE, MCPAgent } from "@aigne/core";
import { OpenAIChatModel } from "@aigne/core/models/openai-chat-model.js";

const { OPENAI_API_KEY } = process.env;

// 1. Initialize the chat model
const model = new OpenAIChatModel({
  apiKey: OPENAI_API_KEY,
  modelOptions: {
    parallelToolCalls: false, // Puppeteer can only run one task at a time
  },
});

// 2. Set up MCP agents for web scraping and filesystem access
const puppeteer = await MCPAgent.from({
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-puppeteer"],
  env: process.env as Record<string, string>,
});

const filesystem = await MCPAgent.from({
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-filesystem", import.meta.dir],
});

// 3. Define the finder agent to research information
const finder = AIAgent.from({
  name: "finder",
  description: "Find the closest match to a user's request",
  instructions: `You are an agent that can find information on the web.
You are tasked with finding the closest match to the user's request.
You can use puppeteer to scrape the web for information.
You can also use the filesystem to save the information you find.

Rules:
- do not use screenshot of puppeteer
- use document.body.innerText to get the text content of a page
- if you want a url to some page, you should get all link and it's title of current(home) page,
then you can use the title to search the url of the page you want to visit.
  `,
  skills: [puppeteer, filesystem],
});

// 4. Define the writer agent to save the report
const writer = AIAgent.from({
  name: "writer",
  description: "Write to the filesystem",
  instructions: `You are an agent that can write to the filesystem.
  You are tasked with taking the user's input, addressing it, and
  writing the result to disk in the appropriate location.`,
  skills: [filesystem],
});

// 5. Create the orchestrator agent to manage the workflow
const agent = OrchestratorAgent.from({
  skills: [finder, writer],
  maxIterations: 3,
  tasksConcurrency: 1, // Puppeteer can only run one task at a time
});

// 6. Initialize the AIGNE instance
const aigne = new AIGNE({ model });

// 7. Invoke the workflow with a detailed prompt
const result = await aigne.invoke(
  agent,
  `\
Conduct an in-depth research on ArcBlock using only the official website\
(avoid search engines or third-party sources) and compile a detailed report saved as arcblock.md. \
The report should include comprehensive insights into the company's products \
(with detailed research findings and links), technical architecture, and future plans.`,
);
console.log(result);