FlowLynk - Build Smart AI Agents with Chain of Thought in Minutes

FlowLynk is a TypeScript package that simplifies building AI-powered agents with LLMs via OpenAI's SDK. It uses Chain of Thought (CoT) reasoning to break down complex problems into logical steps.

Why FlowLynk?

• Chain of Thought Reasoning: Uses CoT to reason through tasks logically, step by step.
• Universal LLM Support: Works with OpenAI, Gemini, and other LLMs compatible with OpenAI's SDK.
• Modular Workflows: Define custom steps, tools, examples and prompts for tailored pipelines.
• Robust Tool Integration: Seamlessly incorporate external tools with input as arguments.
• Error Resilience: Handles API failures, tool errors, and invalid inputs gracefully.
• Developer-Friendly: TypeScript-ready with step-by-step execution and real-time callbacks.
• Beginner-Friendly: Simple error messages and TypeScript safety.
• Clear Process: Tracks each step for transparency and debugging.

Installation

Install via npm:

npm install flowlynk

Create a .env file in your project root and add your LLM provider's API key:

GEMINI_API_KEY=your-openai-api-key-here
# OR
GEMINI_API_KEY=your-gemini-api-key-here

Quick Start

Here's a simple example to create an AI agent that answers a question using Chain of Thought:

import  createLynk  from 'flowlynk';

const lynk = createLynk({
  apiKey: process.env.GEMINI_API_KEY || '',
  model: 'gemini-2.0-flash',
  url: "https://generativelanguage.googleapis.com/v1beta/openai",
  steps: [
    { name: 'think', purpose: 'Reason about the query' },
    { name: 'explain', purpose: 'Provide the final answer' },
  ],
});

async function runLynk() {
  const { result } = await lynk.run('Why is the sky blue?',(step) => {
    console.log('Step:', step);
  });
  console.log('Result:', result);
}

runLynk();

This code creates an agent that thinks through the question and answers (e.g., "The sky is blue due to Rayleigh scattering..."). It demonstrates FlowLynk's CoT approach in action.

What Can FlowLynk Automate?

FlowLynk uses Chain of Thought to automate tasks by reasoning step-by-step. Here are some examples:

• Task Planning: Plan a project or schedule by breaking it into steps.
• Code Generation: Write code files after planning the structure.
• Data Analysis: Analyze datasets and summarize findings.
• Content Creation: Draft emails or reports with structured outlines.
• File Organization: Sort or rename files based on logical rules.
• Workflow Automation: Automate repetitive tasks with clear reasoning.

Usage Examples

Example 1: Task Planning

This example plans a study schedule by reasoning through priorities and automating a to-do list:

import  createLynk  from 'flowlynk';

const lynk = createLynk({
  apiKey: process.env.GEMINI_API_KEY || '',
  model: 'gemini-2.0-flash',
  url: "https://generativelanguage.googleapis.com/v1beta/openai",
  tools: {
    saveList: {
      function: async ({ content }: { content: string }) => {
        try {
          const fs = require('fs').promises;
          await fs.writeFile('todo.txt', content, 'utf8');
          return { status: 'success', message: 'To-do list saved' };
        } catch (error) {
          return { status: 'error', message: (error as Error).message };
        }
      },
      description: 'Saves a to-do list to a file',
      input: { content: 'string' },
    },
  },
  steps: [
    { name: 'analyze', purpose: 'Understand study needs' },
    { name: 'plan', purpose: 'Create a prioritized schedule' },
    { name: 'save', purpose: 'Save the schedule to a file' },
  ],
});

async function runLynk() {
  const { result } = await lynk.run('Plan a study schedule for math and history exams',(step) => {
    console.log('Step:', step);
  });
  console.log('Final Result:', result);
}

runLynk();

This agent analyzes the exams, plans a schedule (e.g., "Day 1: Math, Day 2: History"), and saves it to todo.txt, showing CoT reasoning and automation.

Example 2: Code Generation

This example generates a Python script after planning its structure:

import  createLynk  from 'flowlynk';

const lynk = createLynk({
  apiKey: process.env.GEMINI_API_KEY || '',
  model: 'gemini-2.0-flash',
  url: "https://generativelanguage.googleapis.com/v1beta/openai",
  tools: {
    writeFile: {
      function: async ({ path, content }: { path: string; content: string }) => {
        try {
          const fs = require('fs').promises;
          await fs.writeFile(path, content, 'utf8');
          return { status: 'success', message: `Wrote to ${path}` };
        } catch (error) {
          return { status: 'error', message: (error as Error).message };
        }
      },
      description: 'Writes content to a file',
      input: { path: 'string', content: 'string' },
    },
  },
  steps: [
    { name: 'analyze', purpose: 'Determine script requirements' },
    { name: 'plan', purpose: 'Outline the script structure' },
    { name: 'write', purpose: 'Write the script to a file' },
  ],
});

async function runLynk() {
  const { result } = await lynk.run('Create a Python script to calculate square roots',(step) => {
    console.log('Step:', step);
  });
  console.log('Result:', result);
}

runLynk();

This agent reasons about the script's needs, plans the code, and writes it to a file (e.g., script.py with math.sqrt), automating code generation with CoT.

Example 3: Data Analysis

This example analyzes a dataset and summarizes findings, reasoning through the data first:

import createLynk from 'flowlynk';

const lynk = createLynk({
  apiKey: process.env.GEMINI_API_KEY || '',
  model: 'gemini-2.0-flash',
  url: "https://generativelanguage.googleapis.com/v1beta/openai",
  tools: {
    saveSummary: {
      function: async ({ content }: { content: string }) => {
        try {
          const fs = require('fs').promises;
          await fs.writeFile('summary.txt', content, 'utf8');
          return { status: 'success', message: 'Summary saved' };
        } catch (error) {
          return { status: 'error', message: (error as Error).message };
        }
      },
      description: 'Saves a summary to a file',
      input: { content: 'string' },
    },
  },
  steps: [
    { name: 'analyze', purpose: 'Examine the dataset' },
    { name: 'summarize', purpose: 'Generate a summary' },
    { name: 'save', purpose: 'Save the summary' },
  ],
});

async function runLynk() {
  const { result } = await lynk.run('Analyze sales data: [100, 150, 200, 120]',(step) => {
    console.log('Step:', step);
  });
  console.log('Result:', result);
}

runLynk();

This agent analyzes the sales data, summarizes trends (e.g., "Average sales: 142.5"), and saves the summary to summary.txt, using CoT for reasoning.

Advanced Example with Weather Tool

For more complex workflows, FlowLynk supports custom tools, predefined steps, and step-by-step callbacks:

import createAgent from 'flowlynk';

const agent = createAgent({
  apiKey: process.env.GEMINI_API_KEY || '',
  model: 'gemini-2.0-flash',
  url: "https://generativelanguage.googleapis.com/v1beta/openai",
  tools: {
    getWeather: {
      function: async ({ city }: { city: string }) => ({
        city: city,
        temperature: '25°C', // Mock response
      }),
      description: 'Fetches weather data for a city',
      input: { city: 'string' },
    },
  },
  steps: [
    { name: 'plan', purpose: 'Analyze the query and plan the approach' },
    { name: 'execute', purpose: 'Execute the planned actions using tools' },
  ],
  examples: [
    {
      UserQuery: 'What\'s the weather in London?',
      Output: [
        { step: 'initiate', content: 'Analyzing query to fetch weather' },
        { step: 'action', function: 'getWeather', input: { city: 'London' } },
        { step: 'output', content: 'The temperature in London is 25°C' },
      ],
    },
  ],
});

async function runAdvancedAgent() {
  const { result, steps } = await agent.run(
    'What\'s the weather in New York?',
    (step) => console.log('Current Step:', step)
  );
  console.log('Final Result:', result);
  console.log('All Steps:', steps);
  console.log('Message Logs:', agent.messageLogs());
}

runAdvancedAgent();

Configuration Options

FlowLynk accepts a configuration object with the following options:

| Option | Type | Default | Description | |--------|------|---------|-------------| | apiKey | string | Required | API key for the LLM provider. | | model | string | Required | LLM model (e.g., gpt-4o). | | tools | Record<string, Tool> | {} | Custom tools with functions, descriptions, and input schemas. | | steps | LynkStep[] | undefined | Predefined steps for custom workflows. | | examples | Example[] | [] | Example queries and outputs to guide the agent. | | userSystemPrompt | string | undefined | Custom system prompt to override default behavior. | | history | ChatCompletionMessageParam[] | [] | Message history for context-aware conversations. | | temperature | number | null | Temperature setting for response generation. | | maxTokens | number | null | Maximum token limit for responses. | | url | string | undefined | Custom base URL for the LLM API (optional). |

Features

• Chain of Thought: Reasons step-by-step like a human for complex problem solving.
• Structured Workflow: Enforces initiate → intermediate steps → output.
• Tool Execution: Safely runs custom tools with validated inputs and error handling.
• Step Tracking: Access all steps for debugging or logging.
• Message Logs: Retrieve conversation history with agent.messageLogs().
• Reset State: Clear history and steps with agent.reset() for fresh runs.
• Error Handling: Automatically handles API failures, parsing errors, and tool issues with detailed feedback.

Happy building with FlowLynk!