Using Agentic AI Frameworks in Google Colab

by

Agentic AI is the next frontier in artificial intelligence. Unlike traditional models that only respond to prompts, agentic AI systems can reason, plan, make decisions, and take actions across multiple steps to achieve goals. These systems are particularly useful in automation, tool use, research workflows, and dynamic environments.

Thanks to Google Colab’s powerful cloud-based infrastructure, it’s easier than ever to experiment with agentic AI frameworks like LangChain, AutoGPT, CrewAI, and OpenAgents. If you’re wondering how to get started with using agentic AI frameworks in Google Colab, this article provides a comprehensive, beginner-friendly guide.

What Is Agentic AI?

Agentic AI refers to systems that act like agents. They can:

  • Make decisions based on goals and context
  • Use tools (e.g., web search, APIs, databases)
  • Maintain memory and history of interactions
  • Chain tasks and delegate subtasks autonomously

Agentic frameworks leverage large language models (LLMs) as the reasoning core but add the ability to operate over time, invoke functions, and interact with external systems.

Why Use Google Colab for Agentic AI?

  • Free GPU/TPU support for advanced LLM tasks
  • No setup hassle: Everything runs in your browser
  • Integration with Hugging Face, LangChain, and more
  • Cloud storage via Google Drive
  • Perfect for prototyping and demos

Step 1: Launch Google Colab and Set Up Runtime

To begin using agentic AI frameworks, start by setting up a clean and capable environment in Google Colab:

  1. Visit Google Colab: Navigate to https://colab.research.google.com in your browser.
  2. Sign In: Make sure you’re signed in with your Google account to enable saving and access to Google Drive.
  3. Create a New Notebook: Click the “New Notebook” button in the bottom-right corner of the dialog box. This will open a fresh Jupyter notebook environment.
  4. Rename Your Notebook: Click on the default filename at the top (e.g., Untitled0.ipynb) and rename it to something relevant like agentic_ai_colab_setup.ipynb.
  5. Set Runtime Options:
    • Go to the top menu and click Runtime > Change runtime type
    • Set the Runtime type to Python 3 (or Python 3.10 if available)
    • Set the Hardware accelerator to GPU (or leave as None if GPU isn’t needed)
    • Click Save to apply the changes.

Now your Colab environment is ready to begin installing and using agentic AI tools.

Step 2: Install Required Packages

After setting up your Colab notebook, the next step is to install the libraries required for running agentic AI frameworks.

Paste and run the following commands in a code cell:

!pip install langchain openai tiktoken
!pip install llama-index google-search-results
!pip install faiss-cpu
!pip install unstructured pdfminer.six chromadb

Explanation of Key Libraries:

  • langchain: The core framework for creating agents that can reason and use tools
  • openai: Required to access GPT models via OpenAI’s API
  • tiktoken: Used for token counting and managing prompt length
  • llama-index (GPT Index): For document loading and question answering
  • google-search-results: Enables SerpAPI tool integration
  • faiss-cpu: Vector database support for memory and retrieval
  • unstructured/pdfminer.six: Document parsing tools
  • chromadb: Lightweight embedding-based database for local storage

Wait for the installation to finish before proceeding. This step ensures all dependencies are in place to build and run agentic AI workflows.

Step 3: Set Up API Keys

To use agentic AI in Colab, you need access to large language models like GPT-4 and tools like SerpAPI. Start by setting your API keys securely.

1. Set Your OpenAI API Key:

import os
os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"

Make sure to replace "your_openai_api_key_here" with your actual API key from https://platform.openai.com/account/api-keys.

2. Optional: Set Your SerpAPI Key (for web search tools):

os.environ["SERPAPI_API_KEY"] = "your_serpapi_key_here"

You can sign up and get your SerpAPI key from https://serpapi.com.

Note: Never expose these keys publicly. Use environment variables or Google Colab secrets for safety if collaborating.

Once your keys are set, you’re ready to initialize agentic workflows.

Step 4: Build a Simple LangChain Agent

LangChain makes it easy to build agents that can decide what tools to use and when. Below is a basic example to get started.

Example: Create a Zero-Shot Agent with Web Search and Math Tools

from langchain.agents import initialize_agent, load_tools
from langchain.llms import OpenAI
from langchain.agents.agent_types import AgentType

# Initialize the LLM
llm = OpenAI(temperature=0)

# Load tools (web search and Python REPL)
tools = load_tools(["serpapi", "python"], llm=llm)

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

# Run a sample task
agent.run("Search the latest AI news and calculate the average of 45, 90, and 135")

This agent is capable of:

  • Using the internet to answer questions
  • Performing calculations using Python
  • Chaining reasoning steps to solve composite tasks

Step 5: Explore Advanced Frameworks

Once you’re comfortable with basic LangChain agents, you can explore more powerful agentic AI frameworks. These tools enable autonomous planning, memory, multi-agent collaboration, and more complex workflows.

1. AutoGPT

AutoGPT is a framework where you define a goal and let the agent create its own steps to accomplish it. You can install and run lightweight versions in Colab, though full functionality may require a local runtime.

Example usage is more suited for local development, but you can experiment with simplified logic in notebooks by writing agents that:

  • Receive a high-level goal
  • Break it into subtasks
  • Execute and monitor progress

2. CrewAI

CrewAI allows coordination between multiple AI agents with specific roles (e.g., researcher, summarizer, reporter). Great for multi-turn, collaborative workflows.

Example roles:

  • Research Agent: searches and retrieves content
  • Summary Agent: condenses findings
  • Report Agent: compiles final output

3. OpenAgents

OpenAgents can perform in-depth research using Google search, page parsing, and reasoning. They are especially useful for:

  • Building autonomous researchers
  • Automating workflows like competitive analysis
  • Deep-diving into unfamiliar topics

Each of these tools may require setting up additional APIs, databases, or memory systems. Start with LangChain and progress toward these frameworks as your workflow matures.

  • AutoGPT: Set up goals and let the agent self-direct its plan.
  • CrewAI: Coordinate multiple agents with roles and tasks.
  • OpenAgents: Research-focused framework that chains Google search, reading, and synthesis.

Each framework may require additional setup and memory. Always monitor Colab’s runtime usage.

Use Cases of Agentic AI in Colab

  • Automated research: Summarize articles, extract citations, write literature reviews.
  • Workflow automation: Scrape data, run analyses, and generate reports.
  • Customer support: Multi-turn conversations with context memory.
  • Data pipeline orchestration: Use agents to control processing steps.

Tips and Best Practices

  • Save checkpoints and results to Google Drive
  • Use try/except blocks to handle agent failures
  • Monitor GPU/RAM usage in Colab to avoid crashes
  • Use verbose logging to trace agent decision-making

Limitations to Keep in Mind

  • Free Colab has session limits (~12 hrs)
  • Some models may exceed memory limits
  • LLM APIs can be costly over long runs
  • Real-world agents need fine-tuning and safety layers

Final Thoughts

Exploring agentic AI frameworks in Google Colab allows anyone with a browser to build intelligent, autonomous systems. By combining LLMs with tool use, decision-making, and persistent memory, developers can create agents that perform multi-step tasks without ongoing human intervention.

Start with LangChain, then graduate to more powerful frameworks like AutoGPT and CrewAI to build your own AI copilots, research bots, and automation agents—all from within Google Colab.

Ready to go deeper? Try combining agentic and generative AI in a single notebook!

Leave a Comment