"Build Your AI Agent: A Step-by-Step Guide!"

This guide walks you through creating and deploying a custom AI agent using OpenAI's API and LangChain, from installing libraries and setting up an API key to testing, expanding functionality, and deploying with frameworks like Flask. It also emphasizes the importance of monitoring and maintaining your AI agent for optimal performance.


Step Description
1. Install Required Libraries
Begin by setting up your environment. Install Python and the necessary libraries such as openai, langchain, and dotenv for managing environment variables. Run the following commands: 
pip install openai langchain python-dotenv
2. Get OpenAI API Key
Sign up for an OpenAI account at OpenAI Platform. Once your account is set up, navigate to the API Key section and generate your API key. Store this key securely in a .env file for easy access in your project.
3. Create a .env File
Create a .env file in your project directory to store the OpenAI API key. Add the following line to the file: 
OPENAI_API_KEY=your_api_key_here
Replace your_api_key_here with the API key you generated.
4. Initialize LangChain
Import LangChain and OpenAI libraries into your Python script. Use LangChain to easily integrate OpenAI models and build your AI agent. Example setup: 
from langchain.llms import OpenAI
from dotenv import load_dotenv
import os

load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

llm = OpenAI(api_key=api_key, model="text-davinci-003")
5. Define Your AI Agent
Customize your AI agent by defining its behavior, context, and capabilities. You can use LangChain's chain-building features for this. Example: 
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

prompt_template = PromptTemplate(
    input_variables=["question"],
    template="You are a helpful assistant. Answer the following question: {question}"
)

chain = LLMChain(llm=llm, prompt=prompt_template)
6. Test Your AI Agent
Test your AI agent by providing a question or prompt. Use the run method to get a response. Example: 
response = chain.run(question="What is the capital of France?")
print(response)
The AI agent will respond with the appropriate answer.
7. Expand Functionality
Enhance your AI agent by adding more chains, custom prompts, or integrating third-party APIs. You can use LangChain's modular design to create complex workflows and interactions.
8. Deploy Your AI Agent
Once your AI agent is ready, deploy it using frameworks like Flask, FastAPI, or Django to create an accessible API or web interface. Example with Flask: 
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/ask', methods=['POST'])
def ask():
    data = request.get_json()
    question = data["question"]
    response = chain.run(question=question)
    return jsonify({"response": response})

if __name__ == '__main__':
    app.run(port=5000)
9. Monitor and Maintain
Continuously monitor your AI agent’s performance and update it as needed. Keep track of API usage to avoid exceeding quotas, and periodically refine the prompts and chains to improve accuracy and efficiency.



10-integrating-ai-agents-with    11-security-considerations-fo    12-multi-agent-systems-how-ai    13-evaluating-ai-agents-metri    2-how-ai-agents-work-architec    3-types-of-ai-agents-reactive    4-from-virtual-assistants-to-    5-frameworks-for-building-ai-    6-how-to-build-your-own-ai-ag    7-ai-agents-vs-traditional-bo   

Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next