@aigne/example-workflow-code-execution NPM

Workflow code-execution Demo

This is a demonstration of using AIGNE Framework to build a code-execution 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)
coder(Coder)
sandbox(Sandbox)

coder -.-> sandbox
sandbox -.-> coder
in ==> coder ==> 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 coder processing
class sandbox processing

Workflow of a code-execution between user and coder using a sandbox:

sequenceDiagram

participant User
participant Coder as Agent Coder
participant Sandbox as Agent Sandbox

User ->> Coder: 10! = ?
Coder ->> Sandbox: "function factorial(n) { return n <= 1 ? 1 : n * factorial(n - 1) } factorial(10)"
Sandbox ->> Coder: { result: 3628800 }
Coder ->> User: The value of \(10!\) (10 factorial) is 3,628,800.

Prerequisites

Node.js 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-code-execution

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

# Use pipeline input
echo "Calculate 15!" | npx -y @aigne/example-workflow-code-execution

Installation

Clone the Repository

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

Install Dependencies

cd aigne-framework/examples/workflow-code-execution

pnpm install

Setup Environment Variables

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

OPENAI_API_KEY="" # Set your OpenAI API key here

Run the Example

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

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

# Use pipeline input
echo "Calculate 15!" | 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 "Calculate 15!" | pnpm start

Example

The following example demonstrates how to build a code-execution workflow:

import assert from "node:assert";
import { AIAgent, AIGNE, FunctionAgent } from "@aigne/core";
import { OpenAIChatModel } from "@aigne/core/models/openai-chat-model.js";
import { z } from "zod";

const { OPENAI_API_KEY } = process.env;
assert(OPENAI_API_KEY, "Please set the OPENAI_API_KEY environment variable");

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

const sandbox = FunctionAgent.from({
  name: "evaluateJs",
  description: "A js sandbox for running javascript code",
  inputSchema: z.object({
    code: z.string().describe("The code to run"),
  }),
  process: async (input: { code: string }) => {
    const { code } = input;
    // biome-ignore lint/security/noGlobalEval: <explanation>
    const result = eval(code);
    return { result };
  },
});

const coder = AIAgent.from({
  name: "coder",
  instructions: `\
You are a proficient coder. You write code to solve problems.
Work with the sandbox to execute your code.
`,
  skills: [sandbox],
});

const aigne = new AIGNE({ model });

const result = await aigne.invoke(coder, "10! = ?");
console.log(result);
// Output:
// {
//   $message: "The value of \\(10!\\) (10 factorial) is 3,628,800.",
// }