Introduction

You’ve seen the hype. GPT-powered agents that schedule meetings, scrape the web, write proposals, and handle entire workflows.

What if you could build one yourself—in under an hour?

Thanks to tools like LangChain and OpenAI, you can.

At BeGig, AI freelancers are already using agent stacks to deliver value to clients—whether it’s building GPT-based customer assistants or deploying internal tools powered by LangChain.

In this guide, we’ll show you how to go from zero to live agent using LangChain + OpenAI in 60 minutes or less.

No ML degree. No complicated infrastructure. Just clean, functional workflows that you can expand, sell, or showcase in your BeGig profile.

Who This Is For

This guide is perfect for:

• Freelance developers building LLM tools or assistants
• No-code builders curious about LangChain + GPT
• AI enthusiasts who want a working project, fast
• Prompt engineers learning about tools, memory, and agents
• Founders and consultants looking to build internal AI helpers

Why BeGig Works for AI Agent Builders

BeGig is the go-to platform for freelance AI builders. Here’s why it fits perfectly:

• Tags like LangChain, agentic workflows, and GPT automation help freelancers get discovered
• Projects are tech-first — no vague “build chatbot” listings
• Clients range from early-stage startups to enterprise teams building internal tooling
• Freelancers can productize agents or sell them as integrations

Whether you’re building for clients or just testing your skills, BeGig is where technical talent gets noticed.

🤖 What Is an AI Agent?

An AI agent is an LLM-powered entity that can:

• Take in input
• Use tools (like APIs, search, or code execution)
• Reason and decide what step to take next
• Respond with contextual output

Unlike simple prompt-and-response GPT setups, agents can:

• Chain actions together
• Decide which tools to invoke
• Maintain state or memory

LangChain provides a framework to build such agents with modular tools and workflows.

🧰 What You’ll Need (Prerequisites)

• Python 3.8+
• OpenAI API key
• Basic knowledge of terminal and Python scripting
• (Optional) SerpAPI key for search tools
• 1 hour of focus

Install dependencies:

pip install langchain openai python-dotenv

🛠️ Step-by-Step: Build Your First AI Agent

Step 1: Set Up Your Environment

Create a .env file:

OPENAI_API_KEY=your_openai_key

In your script:

import os
from dotenv import load_dotenv
load_dotenv()

Step 2: Create a Simple OpenAI LLM Wrapper

from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(model_name="gpt-4", temperature=0.2)

This is your LLM brain — keep it lean and low-temperature for reliable agent behavior.

Step 3: Define Tools for the Agent

LangChain agents use tools like search, calculator, code execution, etc.

from langchain.agents import load_tools

tools = load_tools(["serpapi", "llm-math"], llm=llm)

These tools allow the agent to answer questions like:

“What’s the population of Sweden plus 10%?”

Step 4: Load a LangChain Agent

from langchain.agents import initialize_agent
from langchain.agents.agent_types import AgentType

agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)

You’ve now initialized a reactive agent — it decides how to answer based on your tools.

Step 5: Ask the Agent Something Smart

response = agent.run("What is the GDP of Japan divided by the population of Canada?")
print(response)

The agent will:

• Search for data
• Do math
• Return the final answer, all on its own

🧠 Add Memory (Optional)

Add memory to keep track of conversation context.

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
agent_with_memory = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=memory,
verbose=True
)

Useful if you're building assistants or internal tools with follow-up context.

💼 Real Agent Use Cases on BeGig

• Customer Support GPT Agent: Answers customer questions from PDFs using RAG
• Data Query Agent: Pulls numbers from Excel + Airtable and generates insights
• Proposal Generator Agent: Generates quotes and drafts using past client data
• Internal SOP Assistant: Answers staff queries from Notion-based policies
• Lead Qualifier Bot: Scores inbound leads and routes them based on rules

🧠 Key Concepts to Learn After This

Once you've built your first agent, level up by exploring:

• LangChain Agents vs Chains
• Tool customization (e.g. calling webhooks, APIs, DBs)
• RAG integration (retrieval-augmented generation)
• Function-calling APIs in GPT-4
• Agent memory types (buffer, summary, token window)
• UI wrappers using Streamlit, FastAPI, Telegram bots

These all turn a basic agent into a revenue-generating product or client deliverable.

🧩 How Freelancers Can Productize Agents

Offer agent-based services like:

• “Build a ChatGPT assistant for your SOPs and help docs”
• “AI-based research agent for your sales team”
• “Client proposal generator powered by your past briefs”
• “GPT bot for internal HR/ops answers”

These are high-ticket services—especially for startups looking to reduce human ops.

✅ Closing CTA

If you’ve just built your first AI agent in under 60 minutes — congrats, you’ve entered the next era of freelancing.

Whether you're a solo dev, a prompt engineer, or an automation pro, LangChain + OpenAI unlocks a new layer of value you can sell.

And at BeGig, we’re matching AI freelancers like you with clients who actually understand what you’re building.

🧠 Skip basic chatbot gigs. Start getting hired for agentic intelligence.

👉 Join BeGig and land your first agent-powered freelance project.