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TL;DR

This lesson shows you how to get started with agentic AI using free LLM options for learning, experimentation, and prototyping. You'll explore APIs from providers like Groq and Google Gemini, or run local models with Ollama—no payments or subscriptions required to follow along. Whether you use Llama, Gemini, or OpenAI models, you have flexible options to support your learning journey.

Getting Ready: Free LLM Options for This Course

In this course, you’ll be using tools like LangChain and LangGraph to build intelligent, goal-driven AI systems. Many lessons include examples that use OpenAI’s API — a popular option, but one that typically requires a paid subscription.

Good news: you can complete this course without needing to pay for API access.

We’ve intentionally designed the curriculum with low usage requirements, so learners can follow along without hitting paywalls. All major projects and hands-on lessons can be completed using free-tier API keys or local models that run entirely on your machine.

In this lesson, you’ll learn about two ways to get started without spending anything:

1. Use Free API Access
   Providers like Groq and Google Gemini offer generous free usage quotas for high-quality LLMs. Some of these even support OpenAI models, so you can stick with familiar prompts while avoiding upfront costs.

2. Run Local Models with Ollama
   If you have a machine with a decent GPU, you can use Ollama to download and run open-source models like Llama 3, Mistral, or Code Llama right on your computer. This option gives you full control and zero API limits — perfect for hands-on learners who want to explore under the hood.

We’ll walk you through how to set up each of these options and explain the tradeoffs — from speed and quality to installation complexity and system requirements.

Option 1: Use a Free Cloud API

You don’t need to run large language models on your own machine to follow this course. Providers like Groq and Google Gemini offer free API access to high-quality models — no credit card or subscription required.

These cloud APIs are:

• Easy to set up
• Well-supported by LangChain
• Fast enough for real-time use
• Free to use (within generous usage and rate limits)

Let’s start with Groq.

Groq: Fast, Free Access to Open-Source LLMs

Groq is an AI infrastructure provider offering free API access to powerful open-source models like Llama 3 and Mixtral, all hosted on their ultra-fast Language Processing Units (LPUs).

You get real-time responses from capable open models — perfect for learning and prototyping.

⚠️ Note: Groq’s free tier includes rate limits and daily caps. These limits are generous enough for this course, but if you're sending lots of requests, you may occasionally need to wait.

Advantages of Using Groq

• ✅ Free to use (within limits) — no credit card required
• ✅ Fast responses — LPUs make requests snappy
• ✅ Developer-friendly — limits support learning and prototyping
• ✅ OpenAI-style interface — easy prompt reuse
• ✅ LangChain ready — works out of the box with this course setup

How to Set Up and Use Groq

Step 1: Sign Up and Get Your Groq API Key

1. Go to https://console.groq.com
2. Sign up (no payment info required)
3. In the dashboard, go to API Keys
4. Click Create API Key
5. Copy your key (starts with gsk_...)

Step 2: Add Your API Key to the Environment

# Option 1: Add to a .env file
GROQ_API_KEY=your_key_here

# Option 2: Export it directly in your terminal
export GROQ_API_KEY=your_key_here

Step 3: Install the LangChain–Groq Package

Install Groq's LangChain integration:

pip install langchain_groq

Step 4: Run a Simple Test Script

Try this simple test script:

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")
response = llm.invoke("What is agentic AI?")
print(response)

If you see a response in your terminal, you’re good to go.

🧪 Try swapping the prompt to something like "Explain vector databases in one paragraph."
Or test something fun like "Write a haiku about AI agents."

Video Walkthrough: Setup and Use Groq’s Free API 🎥

In this video, you’ll learn how to set up Groq’s free API key and load it into your local development environment using both export and .env file methods.

By the end of this video, you’ll be ready to run your own LangChain-powered applications using Groq’s Llama 3 models — no billing or cloud configuration needed.

Gemini: Access Google’s LLMs Through a Free Developer Tier

Gemini is Google’s suite of powerful large language models — and you can access them via Gemini API, using a developer key that you generate through Google AI Studio.

You don’t need to enter payment information to get started. The API offers a free tier designed for testing, development, and learning — making it a great fit for coursework and experimentation.

⚠️ Google’s free tier includes usage limits and rate caps. These are typically generous enough for this course, but they are not meant for production use. If you hit a limit, you can wait for it to reset — or upgrade later if needed.

Advantages of Using Gemini

• ✅ No billing required to start — generate an API key instantly with your Google account
• ✅ Developer-friendly limits — enough for lessons, prototyping, and experimentation
• ✅ Good performance on reasoning and coding tasks
• ✅ Supports multimodal input (text, images, etc.)
• ✅ Works seamlessly with LangChain

How to Set Up and Use Gemini

Step 1: Create Your API Key

1. Go to https://makersuite.google.com
2. Sign in with your Google account
3. Click Get API Key
4. Choose Create API Key in new project
5. Copy your key — it will look like AIza...

Step 2: Add the Key to Your Environment

# Option 1: Add to a .env file
GOOGLE_API_KEY=your_key_here

# Option 2: Export it in your terminal
export GOOGLE_API_KEY=your_key_here

Step 3: Install the Required Package

pip install langchain-google-genai

Step 4: Test Your Setup

Try this minimal script:

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)
response = llm.invoke("Explain prompt engineering in simple terms.")
print(response)

You should see a clean response — ready to go, with no billing setup.

🧪 Tip: Try switching the prompt to "Give me a metaphor for vector embeddings" or "What's a beginner-friendly explanation of LangChain?"

Use Google Gemini’s Free API in Your Projects 🎥

In this video, you’ll learn how to create and configure a Gemini API key using Google AI Studio. You’ll integrate the key into your environment (via .env file or terminal) and send your first request — all in under 5 minutes.

Option 2: Run a Local LLM with Ollama

Don’t want to depend on cloud APIs at all? Ollama lets you run powerful LLMs like Llama 3, Mistral, and Code Llama locally — with no API keys, no rate limits, and no internet required once models are downloaded.

This is a great option if:

• You want full control over the models you're using
• You’re interested in exploring open models
• You have a machine with enough resources (RAM and GPU)

⚠️ Running local models requires decent hardware — especially if you're using larger models. We recommend starting with smaller variants like llama3-8b-8192 if you’re unsure.

Advantages of Using Ollama

• ✅ No API calls or rate limits — everything runs locally
• ✅ Fully offline after initial model download
• ✅ Free and open-source — no subscriptions or tokens needed
• ✅ Great for privacy-conscious development and experimentation
• ✅ LangChain-compatible with minimal setup

What Your System Needs

Going local with Ollama gives you full control and unlimited usage — but it also demands serious memory. Here’s how to figure out if your hardware can handle it.

Memory Rule (Assuming FP32)

Let’s assume you’re running the model using standard FP32 precision — that means each parameter (or weight) uses 4 bytes of memory.

Memory required (in GB) =
Model size in billions × 4

💡 Here, “model size” refers to the number of parameters (also called weights), typically expressed in billions (e.g., 7B, 13B, 70B).

So for example:

• A 7B model in FP32 requires 7 × 4 = 28 GB
• A 13B model in FP32 requires 13 × 4 = 52 GB

Most laptop GPUs can’t handle this — but don’t worry, there’s a trick.

But You Can Use Less: Enter Quantization

Most models don’t need full 32-bit precision to run effectively. You can use quantization — a technique that stores each parameter in fewer bits.

This significantly reduces memory usage while keeping model quality high enough for most tasks.

Memory Formula with Quantization

Memory required (in GB) =
Model size in billions × bytes per parameter

Examples:

• A 7B model in Q4 → 7 × 0.5 = 3.5 GB
• A 13B model in FP16 → 13 × 2 = 26 GB

That’s why tools like Ollama can run quantized models on machines with as little as 8–16 GB of GPU memory.

Memory Requirements by Model Size

Reality Check: Don't Forget Overhead

The values above only account for model weights. But during inference, your system also needs memory for:

• Activations (intermediate outputs at each layer)
• Input tokens
• Temporary buffers

A safe rule of thumb:

Add 20–50% extra memory on top of the base estimate to avoid out-of-memory errors.

What Will Run on a Laptop GPU?

• 8 GB GPU → you’ll likely need to stick with 1B–3B models in Q4
• 16 GB GPU → you might be able to run a 7B model in Q4 or Q8
• 32 GB+ GPU → capable of running 13B+ models, especially in FP16

🔧 For comparison: An NVIDIA A100 (40 GB variant) can run a 13B model in FP16 or a 30B model in Q4. To run 70B models, you'd need multiple high-end GPUs or advanced memory management.

How to Set Up and Use Ollama

Step 1: Install Ollama

Go to https://ollama.com and download the installer for your system (macOS, Windows, or Linux).

Follow the instructions to complete installation.

Step 2: Download a Model

Once installed, open your terminal and run:

# Recommended starting point
ollama pull llama3-8b-8192

Other available models include:

ollama pull mistral
ollama pull codellama

These will download and prepare the models for local use.

Step 3: Install the LangChain Integration

Install the Ollama LangChain wrapper:

pip install langchain-ollama

Step 4: Run a Local LLM Test

Here’s a minimal example:

from langchain_community.chat_models import ChatOllama

llm = ChatOllama(model="llama3-8b-8192")
response = llm.invoke("Summarize how local models work.")
print(response)

This will invoke the LLM running on your machine and return a response — no cloud involved.

🧪 Try a fun test prompt like: "Explain agentic AI like you're a movie trailer narrator."

When to Use Which Option

Not sure which path to pick? Here’s the good news: they all work — and you can switch at any time.

If you want something fast and cloud-hosted, start with Groq or Gemini. Prefer full control or want to work offline? Try Ollama. You’re not locked in — LangChain makes it easy to change providers without rewriting your code.

There’s no single “best” choice — just pick what fits your system and comfort level. You can always switch later.

# Download the 20B model (requires ~16GB RAM)
ollama pull gpt-oss:20b

# Or the larger 120B model (requires ~60GB RAM)
ollama pull gpt-oss:120b

Final Notes

You’re now ready to start your journey building with agentic AI — no cost barriers, no complicated setup.

As you work through the lessons and projects, any of these LLM options will work. You can stick with one, or try more than one to see what fits your style.

Most importantly, you can learn and experiment without burning through tokens or money — it’s easy to stay within the free tiers or run models locally.

And if you ever get stuck, we’re just a message away on Discord.

Next up: we’ll kick off your hands-on journey with prompt engineering — the core skill behind making LLMs useful, flexible, and goal-driven. Now that your setup is ready, it’s time to start building.

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⬅️ Previous - Agents Vs. Workflows
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