# Tony Kipkemboi
> AI Engineer and Content Creator specializing in AI automations, agent systems, and developer education. US Army veteran. Former CrewAI, Snowflake, Bloomberg, Booz Allen Hamilton.
I build AI automations and agent systems that help teams work smarter. I create technical content about AI across social platforms including YouTube, where my most popular video on building PDF RAG systems with Ollama has 189K+ views.
I've spoken at PyCon US, ODSC, Harvard Kennedy School, IBM TechXchange, MLOps World, and more. I'm passionate about open-source software, AI agents, and developer education.
## Contact & Social
- Website: https://tonykipkemboi.com
- GitHub: https://github.com/tonykipkemboi
- YouTube: https://www.youtube.com/@tonykipkemboi
- LinkedIn: https://linkedin.com/in/tonykipkemboi
- X/Twitter: https://x.com/tonykipkemboi
- RSS Feed: https://tonykipkemboi.com/rss
## Expertise
- AI Agents & Multi-Agent Systems (CrewAI, LangChain, LlamaIndex)
- RAG (Retrieval-Augmented Generation)
- Python, Streamlit, Next.js
- Local LLMs (Ollama, Groq)
- Developer Education & Technical Content Creation
## Background
- US Army Veteran
- Former Developer Advocate at CrewAI
- Former Snowflake
- Former Bloomberg
- Former Booz Allen Hamilton
- University of Pennsylvania (Penn Engineering)
---
## Open Source Projects
### Ollama PDF RAG (487 GitHub stars)
A locally-hosted RAG (Retrieval-Augmented Generation) system that allows users to chat with their PDF documents using Ollama and LangChain. Features include document chunking, vector embeddings, and semantic search.
- Link: https://github.com/tonykipkemboi/ollama_pdf_rag
- GitHub: https://github.com/tonykipkemboi/ollama_pdf_rag
- Tech: Python, Ollama, LangChain, Streamlit, ChromaDB
### CrewAI Gmail Automation (175 GitHub stars)
Automate Gmail inbox management using CrewAI agents. Intelligently categorizes, responds to, and organizes emails using AI-powered workflows.
- Link: https://github.com/tonykipkemboi/crewai-gmail-automation
- GitHub: https://github.com/tonykipkemboi/crewai-gmail-automation
- Tech: Python, CrewAI, Gmail API, LangChain
### Resume Optimization Crew (147 GitHub stars)
AI-powered resume optimization system using CrewAI. Analyzes and enhances resumes to match job descriptions and ATS requirements.
- Link: https://github.com/tonykipkemboi/resume-optimization-crew
- GitHub: https://github.com/tonykipkemboi/resume-optimization-crew
- Tech: Python, CrewAI, AI Optimization
### Trip Planner Agent (137 GitHub stars)
CrewAI agents that can plan your vacation. Uses multi-agent collaboration to create detailed itineraries based on your preferences.
- Link: https://github.com/tonykipkemboi/trip_planner_agent
- GitHub: https://github.com/tonykipkemboi/trip_planner_agent
- Tech: Python, CrewAI, Streamlit, LangChain
### Streamlit Replicate Image App (99 GitHub stars)
Image generation application built with Streamlit and Replicate API. Generate AI images using various models through an intuitive interface.
- Link: https://github.com/tonykipkemboi/streamlit-replicate-img-app
- GitHub: https://github.com/tonykipkemboi/streamlit-replicate-img-app
- Tech: Python, Streamlit, Replicate, Image Generation
### Groq Streamlit Demo (85 GitHub stars)
Demo showcasing Groq's ultra-fast LLM inference with Streamlit. Experience lightning-fast AI responses in an interactive web interface.
- Link: https://github.com/tonykipkemboi/groq_streamlit_demo
- GitHub: https://github.com/tonykipkemboi/groq_streamlit_demo
- Tech: Python, Groq, Streamlit, LLM
### Ollama Streamlit Demos (82 GitHub stars)
Collection of Streamlit demos showcasing various Ollama local LLM capabilities. Run AI models locally with no API keys required.
- Link: https://github.com/tonykipkemboi/ollama_streamlit_demos
- GitHub: https://github.com/tonykipkemboi/ollama_streamlit_demos
- Tech: Python, Ollama, Streamlit, Local LLM
### CrewAI Streamlit Demo (66 GitHub stars)
Demo showcasing how to output CrewAI agent task outputs on the Streamlit UI.
- Link: https://github.com/tonykipkemboi/crewai-streamlit-demo
- GitHub: https://github.com/tonykipkemboi/crewai-streamlit-demo
- Tech: Python, CrewAI, Streamlit
### Research Paper to Podcast (65 GitHub stars)
Automated system that transforms academic research papers into engaging podcast conversations using CrewAI and ElevenLabs.
- Link: https://github.com/tonykipkemboi/research-paper-to-podcast
- GitHub: https://github.com/tonykipkemboi/research-paper-to-podcast
- Tech: Python, CrewAI, ElevenLabs
### YouTube Yapper Trapper (65 GitHub stars)
Extract and analyze YouTube video transcripts. Perfect for researchers, content creators, and anyone who wants to quickly digest video content.
- Link: https://github.com/tonykipkemboi/youtube_yapper_trapper
- GitHub: https://github.com/tonykipkemboi/youtube_yapper_trapper
- Tech: Python, YouTube API, Transcription
### Kaa Rada
A modern Pomodoro web app to secure your focus and execute your tasks. Features a timer, music player (add YouTube tracks), task list, and live Hacker News feed. Built with Vercel v0.
- Link: https://v0-kaa-rada-qohixj.vercel.app/
- Tech: Next.js, Vercel v0, Pomodoro, YouTube, Hacker News
### YouTube Thumbnail Extractor
Extract high-quality thumbnails from any YouTube video. Simply paste the URL to get started.
- Link: https://www.downloadthumbnails.com/
- Tech: Next.js, YouTube, Web App
---
## Speaking & Media Appearances
### MLOps World Conference - Austin
- Type: talk
- Source: MLOps World
- Date: 2025-10-08
- Link: https://mlopsworld.com/speakers/
Speaker demonstrating how agent orchestration, paired with rigorous evaluation, accelerates the path from prototype to production.
### IBM TechXchange Conference
- Type: talk
- Source: IBM
- Date: 2025-10-06
- Link: https://www.linkedin.com/posts/tonykipkemboi_ibmtechxchange-activity-7381001218820681728-Njde/
Speaker discussing AI agents and enterprise AI adoption strategies.
### Building AI Agents with CrewAI - DataCamp Course
- Type: course
- Source: DataCamp
- Date: 2025-10-01
- Link: https://www.datacamp.com/courses/building-ai-agents-with-crewai
Comprehensive course teaching developers how to build AI agent systems with CrewAI.
### Creating a Podcast Generation AI Multi-Agent - DataCamp Code-Along
- Type: course
- Source: DataCamp
- Date: 2025-08-13
- Link: https://www.datacamp.com/code-along/creating-a-podcast-generation-ai-multi-agent-with-crew-ai
Interactive code-along tutorial teaching how to use CrewAI to build a multi-agent system.
### ODSC AI X Podcast - AI Agents
- Type: podcast
- Source: Open Data Science Conference
- Date: 2025-06-11
- Link: https://podcasts.apple.com/us/podcast/odsc-east-2025-minisodes/id1721516836?i=1000712490491
Featured on ODSC's AI X Podcast discussing foundational AI agent building skills.
### Convergence 2025 - GenAI Engineering Conference
- Type: talk
- Source: Comet ML
- Date: 2025-05-13
- Link: https://www.comet.com/site/about-us/news-and-events/events/convergence-2025/
Speaking at Comet's virtual conference on GenAI Engineering.
### Build agentic systems with CrewAI and Amazon Bedrock
- Type: article
- Source: AWS Machine Learning Blog
- Date: 2025-03-31
- Link: https://aws.amazon.com/blogs/machine-learning/build-agentic-systems-with-crewai-and-amazon-bedrock/
Co-authored AWS ML Blog post on building agentic systems with CrewAI and Amazon Bedrock.
### ODSC East 2025 Workshop
- Type: talk
- Source: Open Data Science Conference
- Date: 2025-05-13
- Link: https://odsc.com/boston/
Led workshop on 'Agentic AI in Action: Build Autonomous, Multi-Agent Systems Hands-On in Python'.
### Guest Lecture at Harvard Kennedy School
- Type: talk
- Source: Harvard University
- Date: 2025-02-27
- Link: https://www.linkedin.com/posts/tonykipkemboi_aiagents-hks-activity-7301069792810008576-H7os/
Guest speaker on AI agents for Prof. Hu's data and information visualization class.
### PyCon US 2024
- Type: talk
- Source: PyCon US
- Date: 2024-05-15
- Link: https://us.pycon.org/2024/speaker/profile/90/index.html
Selected speaker at PyCon US 2024, the largest annual gathering for the Python programming community.
---
## Blog Posts
### Securing the AI Frontier
- Published: 2025-05-01
- Category: Security
- Tags: AI Security, AI Agents, Enterprise AI, Cybersecurity, Prompt Injection
- URL: https://tonykipkemboi.com/blog/ai-agent-security
AI agents are amplifying the need for AI security. The global AI cybersecurity market is projected to reach $135 billion by 2030.
#### Full Content
My prediction: **_"2025 is the year of AI agents but 2026 will be the year of AI security."_**
We're almost halfway through 2025 and AI agents are already in production!
The next natural evolution is security threats and reports of hacks; because
hackers love exploiting productionized products, especially new and innovative ones.
Most of the cybersecurity companies are already positioning themselves as AI security providers.
Consolidation has started and will continue for the rest of this year.
Palo Alto Networks just acquired Protect AI for over [$500 million](https://www.geekwire.com/2025/palo-alto-networks-acquires-protect-ai/),
highlighting how crucial AI security has become; if it wasn't already.
The global AI cybersecurity market is expanding dramatically—from $25 billion in 2023 to a projected
[$135 billion by 2030](https://lakera.ai/reports/security-trends-2024). Startups and established cybersecurity firms alike are attracting significant investment,
positioning themselves as essential providers of AI security solutions.
LLMs have been known to have security vulnerabilities, and AI agents are going to be no exception. In fact, AI agents just magnify the risk of these vulnerabilities.
## Unique Threats in AI Agent Security
AI agents introduce specific vulnerabilities beyond traditional cybersecurity risks:
- **Data Poisoning**: Attackers deliberately corrupt AI training datasets, resulting in incorrect or malicious outcomes.
- **Prompt Injection**: Adversaries manipulate AI inputs to bypass security controls and cause unintended disclosures.
- **Model Theft**: Proprietary AI models and critical business data risk being stolen, potentially leading to severe competitive disadvantages.
- **Tool Misuse**: Attackers can manipulate AI agents to misuse their integrated tools, potentially triggering harmful actions or exploiting vulnerabilities.
- **Credential Leakage**: Exposed service tokens or secrets can lead to impersonation, privilege escalation, or infrastructure compromise.
- **Unauthorized Code Execution**: Unsecured code interpreters in AI agents can expose systems to arbitrary code execution and unauthorized access.
These are not hypothetical risks. High-profile incidents, including [Microsoft's Bing Chat revealing sensitive internal rules](https://www.zdnet.com/article/microsofts-chatgpt-powered-bing-reveals-its-codename-and-rules-and-argues-with-users/) and [financial frauds involving deepfake technologies](https://www.fincen.gov/news/news-releases/fincen-issues-alert-fraud-schemes-involving-deepfake-media-targeting-financial),
have demonstrated the real-world impact of these threats.
## The Need for Secure AI Agents
As AI agents become more autonomous and powerful, they require specialized security approaches which involve addressing multiple layers:
- The foundation model itself
- The agent framework
- The tools and integrations
- The runtime environment
- The data being processed
Each layer presents unique challenges and requires specific security controls. In a future blog post,
I'll dive into practical implementation strategies for securing CrewAI agents, including code examples.
## AI Agent Security Startups Worth Watching
**[HiddenLayer](https://hiddenlayer.com)**
- Founded: 2019
- Funding: $50M Series A (2023)
- Unique Approach: ML security platform with automated threat detection
**[Protect AI](https://protectai.com) (acquired by [Palo Alto Networks](https://paloaltonetworks.com) in 2025)**
- Founded: 2022
- Funding: $108.5M
- Unique Approach: Secures ML supply chains and DevSecOps integration
**[Robust Intelligence](https://robustintelligence.com) (acquired by [Cisco](https://cisco.com) in 2024)**
- Founded: 2019
- Funding: $53M
- Unique Approach: AI firewall and proactive model validation
**[Lakera](https://lakera.ai)**
- Founded: 2021
- Funding: $20M Series A (2024)
- Unique Approach: Real-time security for generative AI applications
**[CalypsoAI](https://calypsoai.com)**
- Founded: 2018
- Funding: $23M Series A1
- Unique Approach: Validates AI model safety and continuous monitoring
**[Adversa AI](https://adversa.ai)**
- Founded: 2019
- Funding: $8M
- Unique Approach: Focuses on adversarial robustness and protection against ML model attacks
**[Stytch](https://stytch.com)**
- Founded: 2020
- Funding: $123M (Series B in 2022)
- Unique Approach: Passwordless authentication platform enhancing security for AI applications
**Note**: This list is not exhaustive. I'm sure I missed some. Feel free to [reach out to me](https://linkedin.com/in/tonykipkemboi) if you wish to add any.
---
### RBAC for AI Agents
- Published: 2025-04-15
- Category: Security
- Tags: RBAC, AI Agents, Authentication, Access Control, Identity Management, Enterprise AI
- URL: https://tonykipkemboi.com/blog/agent-authentication-rbac
As AI agents become digital workers, organizations must rethink identity, access, and permissions for non-human actors. Here's why agent authentication and fine-grained RBAC will define the next era of AI adoption.
#### Full Content
As AI agents move from proof-of-concept into production, a new set of challenges are emerging. One of them is identity and access management for these non-human actors.
Today, every employee at a company gets a user profile, a set of credentials, and carefully scoped permissions—often managed by sophisticated RBAC (role-based access control) systems. But what about the AI agents that are now reading your emails, updating your CRM, or querying your database?
If agents are to become true digital workers, they'll need to be treated like employees: with profiles, audit trails, and—critically—permissions. Otherwise, we risk creating a shadow workforce with no accountability, no oversight, and massive security risks.
## Why Agent Identity Matters
Agents increasingly act on behalf of users, teams, or entire organizations. If agents are **_anonymous_** or **_over-permissioned_**, they become a new vector for data leaks, fraud, and compliance failures. Just as with human employees, we need to know: who did what, when, and why?
TL;DR, these are the reasons why agent identity and access management are critical:
- **Audit Trails:** Every agent action should be traceable.
- **Accountability:** Agents must operate within clear boundaries.
- **Compliance:** Regulations may soon require agent identity management.
## Agent Profiles: The New User Accounts
Think of an AI agent profile as a digital employee file:
- **Unique Agent Identifier:** How the agent is recognized in the system
- **Credentials** (API keys, OAuth tokens, etc.): What the agent uses to authenticate to services
- **Capabilities:** What the agent is allowed to do
- **Owner/Supervisor:** Who created or manages the agent
- **Context:** Purpose, current task, environment
Agent profiles will enable better management, trust, and lifecycle control (onboarding, offboarding, suspension).
## RBAC for Agents: Roles, Permissions, and Fine-Grained Access
Assigning roles and permissions to agents is not going to be a nice-to-have—it will be a necessity. But the bar is even higher than for humans:
- **Least Privilege:** Agents should only access what's absolutely necessary.
- **Dynamic Permissions:** As agents learn or change roles, their access must update in real time.
- **Revocation:** Removing agent access instantly is critical for security.
### Fine-Grained Data Access: Beyond the Row, Down to the Cell
In many organizations, access controls are not just at the file or table level—they're at the row or even cell level. For example, a sales agent may only see revenue data for their region, or a healthcare agent may see only certain fields in a patient record.
AI agents will need to respect these boundaries:
- **Cell-Level RBAC:** Agents should only read/write the specific data they're authorized for.
- **Context-Aware Policies:** Access rights may depend on the agent's task, user, or even time of day.
- **Auditability:** Every access—especially to sensitive data—must be logged and reviewable.
## The Opportunity: Building the Agent Identity Layer
Just as Okta and Auth0 built massive businesses around human identity, there's a coming wave of startups building identity, RBAC, and lifecycle management for agents. We'll see:
- Agent directories (who are the agents in my org?)
- Permission dashboards
- Automated onboarding/offboarding
- Delegation and escalation workflows
## Challenges and Open Questions
- How do you revoke agent access instantly, everywhere?
- How do you handle agent-to-agent delegation and impersonation?
- What about agents that spawn other agents—who is responsible for their actions?
- How do you ensure explainability and transparency as agents become more autonomous?
## Other Open Questions
- **User Consent:** How do users grant (and revoke) agents permission to act on their behalf?
- **Agent Lifecycle:** What happens to access and data when an agent is retired or replaced?
- **Cross-Org Collaboration:** How are permissions managed when agents work across company or department boundaries?
- **Human-in-the-Loop:** When should humans be able to override or audit agent actions in real time?
- **Privacy:** How do we ensure agents only access the minimum data needed, especially with sensitive info?
- **Impersonation Risks:** How do we prevent fake or hijacked agents?
- **Regulation:** How will new laws and liability shape agent identity and access?
These are just a few of the interesting topics that will shape how we trust and deploy AI agents at scale. We've already dealt with these issues in the human world, and the same principles will apply to agents, but with even more complexity.
## Conclusion
As organizations deploy more AI agents, the need for clear identity and access controls will only grow. The best solutions will balance security, flexibility, and transparency—without getting in the way of what makes agents powerful in the first place.
---
### SaaS Isn't Dying—It's Becoming the Toolbox for AI Agents
- Published: 2025-02-02
- Category: Industry Insights
- Tags: SaaS, AI Agents, Automation, Enterprise AI, API Design
- URL: https://tonykipkemboi.com/blog/saas-vs-agents
SaaS isn't disappearing—it's evolving into the backend infrastructure that AI agents use to get work done.
#### Full Content
There's been a lot of talk about AI agents *killing* SaaS.
This is not entirely true in my opinion.
SaaS isn't disappearing—it's evolving into **the backend infrastructure that AI agents use to get work done**.
The real shift isn't about replacing software. It's about **who (or what) is using it**.
Right now, humans are the primary users of SaaS tools.
Soon, **AI agents will be the primary users**—handling workflows autonomously while humans focus on strategy.
But here's the thing: **AI agents don't work without SaaS**. They need software to connect to, APIs to call, and data to pull from.
## AI Agents Aren't Replacing SaaS, They're Becoming the New Users
Let's talk about what this actually means.
Today, a salesperson logs into:
- HubSpot to check leads
- LinkedIn to send DMs
- Gmail to follow up
- Notion to take notes
Soon, an AI sales agent will do all of that:
- Monitor CRM activity
- Draft and send follow-up emails
- Auto-update deal statuses
- Schedule meetings in Calendly
But does that mean HubSpot is dead? Nope.
It just means the user is no longer a human clicking buttons—it's an AI agent making API calls.
The same pattern will play out across **every industry**.
## Marketing: No More Clicking Around in CRMs
Today:
- Marketers log into HubSpot, Salesforce, or Mailchimp to launch campaigns.
- They analyze performance, tweak copy, and optimize manually.
Soon:
- An AI agent will auto-adjust campaigns based on real-time data.
- It will rewrite ad copy dynamically, adjust budgets, and refine targeting *without human intervention*.
But what is it using? **The same SaaS tools—just better**.
HubSpot doesn't die—it just becomes **an invisible engine powering AI-driven marketing**.
## HR & Recruiting: AI Agents Will be Hiring for You
Right now, recruiters:
- Manually post jobs, screen resumes, and email candidates.
- Waste hours scheduling interviews.
Soon, an AI hiring agent will:
- Auto-post jobs based on hiring needs.
- Analyze resumes before a human ever sees them.
- Send outreach emails and schedule interviews dynamically.
Does that mean Workday, Lever, or Greenhouse are obsolete? No.
They just become **tools that AI agents use to recruit at scale**.
## Finance & Ops: AI Agents Will Run Your Books
CFO workflows today:
- Checking QuickBooks, Stripe, and Expensify for financial insights.
- Approving expenses and running forecasts manually.
Soon, AI-powered CFO agents will:
- Pull financial reports automatically.
- Flag unusual transactions for review.
- Predict cash flow trends and recommend cost-saving moves.
But they're not doing this in a vacuum—they're still calling on **QuickBooks, Expensify, and Tableau**.
The difference? **No human is clicking through dashboards anymore**.
## Now, What if AI Agents Build Their Own SaaS?
What if AI agents start building their own SaaS—then other AI agents consume that software on behalf of users?
The AI agents will be working in **crews**, much like departments in an organization:
- One group of AI agents could develop and maintain a customer relationship platform.
- Another crew might build a content management system for marketing.
- Yet another team could create specialized analytics tools.
In this scenario, **AI agents create, integrate, and consume SaaS tools in a fully autonomous cycle**. The hierarchy might mirror modern org structures, with different AI **"departments"** handling specific tasks.
This evolution doesn't spell the end of SaaS. It just means that the very fabric of SaaS will be woven by AI.
- Even if AI agents build their own tools, the underlying model is the same: **modular, API-driven services**.
- SaaS products will still be the building blocks, only now they'll be crafted and orchestrated entirely by AI agents.
Whether built by humans or AI, these tools must deliver robust, specialized capabilities that are hard to replicate in-house. The value remains in the quality, security, and efficiency of the software—qualities that make these tools indispensable.
So, while the players might change, the game stays the same: if your platform isn't built for AI-driven integration and autonomous operation, you're falling behind TBH.
## The Future of SaaS is Invisible
The biggest SaaS brands of the next decade will be **the ones you don't even log into**.
AI agents will interact with them so seamlessly that they'll disappear into the background.
UI will be a thing of the past as AI agents will be able to interact with your software in natural language and not through a UI.
This is the shift: SaaS isn't going away. It's just becoming the infrastructure behind AI-driven work.
So what should SaaS companies do now?
- Prioritize API-first design – make it easy for AI agents to use your product.
- Build automation-first features – AI agents will be your next big user base.
- Move beyond UI-driven workflows – the future isn't dashboards, it's direct integrations.
The question isn't *"Will AI kill SaaS?"*
The real question is: **Is your SaaS ready for AI to be its biggest customer?**
---
### You're Leading the AI Revolution
- Published: 2025-01-22
- Category: Industry Insights
- Tags: Consumer AI, AI Agents, Enterprise AI, AI Adoption, Technology Trends
- URL: https://tonykipkemboi.com/blog/consumers-leading-ai-revolution
Discover how consumers are outpacing enterprises in AI adoption and why this shift is redefining the future of technology.
#### Full Content
AI is everywhere right now. If you've ever asked ChatGPT a random question or played around with a fun avatar generator, congratulations—you're part of the AI revolution and you're leading the charge in AI adoption.
What's wild is this: consumers (that's you and me) are outpacing enterprises when it comes to jumping on the AI train. That's a complete flip from most other tech cycles. Usually, the big corporations figure things out first, and we eventually get integrated as part of a product offering.
So, why's it different this time? Here's my take:
#### 1. AI is Ridiculously Accessible
You don't need a PhD, a fat wallet, or some corporate information technology team to get started with AI. Platforms like ChatGPT, Midjourney, and CrewAI are just... there. Free trials, low-cost plans, and easy interfaces mean you can start playing with AI in minutes.
#### 2. We're Selfish (in the Best Way)
AI tools solve our everyday problems **right now**. Need a workout plan? Ask ChatGPT. Struggling with meal ideas? Ask Claude. Want a cool birthday card design? Use Midjourney or Replicate to generate an image. Want to automate a list of complex tasks? Use CrewAI.
Consumers are using AI for fun, creativity, and productivity, while enterprises are still figuring out how it fits into their big-picture strategies.
#### 3. Most Contagious Thing Since 'Baby Shark'
AI-generated stuff is *everywhere*. Your friend's new profile pic, a viral tweet, or even that hilarious AI-written song—it's all over social media. People see it, think "I want to try that," and boom—another new user.
#### 4. Businesses are Stuck in Their Own Red Tape
Big companies have way more to think about: compliance, integrating AI into old systems, and making sure customer data isn't leaked everywhere. For the rest of us? We just care if it works.
This is not a negative per se as there are industries that need tighter regulations around AI, such as healthcare, finance, and military.
#### 5. Cost-Effective for Individuals
Most AI tools have free plans or cost less than your coffee habit. For enterprises, scaling these tools means bigger costs, more negotiations, and longer approval processes.
#### 6. Consumer-First Innovation
In past tech cycles, innovation was usually built for companies first. Think about early computers or software—businesses got the shiny new toys, and consumers had to wait. With AI, it's flipped. Companies like OpenAI and Meta are focusing on consumer consumption more.
## So What Does This Mean?
We are driving AI innovation right now. We're the beta testers, the explorers, and the ones pushing these tools into the spotlight. Enterprises will catch up (they always do), but for now, the playground belongs to us.
Enjoy it. Create. Experiment. And keep leading the way.
---
### Get Started with AI Agents Using CrewAI
- Published: 2024-12-12
- Category: Tutorials
- Tags: AI Agents, CrewAI, LLMs, Automation, Tutorial
- URL: https://tonykipkemboi.com/blog/crewai-quickstart
Learn how to build your first AI agent using CrewAI, a framework for creating autonomous AI agents that can work together to accomplish complex tasks.
#### Full Content
As a developer advocate at [CrewAI](https://crewai.com), I get asked a lot about how to get started with building AI agents.
In this brief blog post, I'll walk you through the process of getting started with CrewAI and creating your first AI agent.
## What is CrewAI?
CrewAI is an innovative framework designed to orchestrate role-playing AI agents. It allows you to create autonomous AI agents that can:
- Work together in a hierarchical structure
- Share context and information
- Execute complex tasks sequentially or in parallel
- Integrate with various LLM providers
## Setting Up Your Environment
First, you'll need to install CrewAI and CrewAI tools:
```bash
pip install crewai crewai-tools
```
You'll also need to configure your LLM provider. CrewAI supports various options including:
- OpenAI
- Anthropic
- Local models via Ollama
- Google Vertex AI
- Azure OpenAI
- [More here](https://docs.crewai.com/concepts/llms#provider-configuration-examples)
## Creating Your First Agent
There are [various ways](https://docs.crewai.com/concepts/agents) to create an agent, but I'll show you how to create a simple agent in CrewAI.
First, configure your API keys:
```bash
# Get your free API key here: https://serper.dev
export SERPER_API_KEY='your_serper_api_key'
```
Then create a file called **"main.py"** and add the following code:
```python
from crewai import Agent, Task, Crew, Process, LLM
from crewai.tools import SerperDevTool
# Create an LLM provider
llm = LLM(
model='o1-preview',
api_key='your_openai_api_key',
temperature=0.7
)
# Create a research agent
researcher = Agent(
role='Research Analyst',
goal='Conduct detailed research on AI technology trends',
backstory="""You are an expert research analyst with a focus on AI technology.
You have a track record of identifying emerging trends and providing actionable insights.""",
tools=[SerperDevTool()],
llm=llm,
verbose=True
)
```
## Defining Tasks
Tasks are what agents need to accomplish:
```python
# Create a task
research_task = Task(
description="""Analyze the latest developments in AI agents and autonomous systems.
Focus on real-world applications and emerging trends.""",
expected_output="An executive summary of comprehensive insights into the current state of AI agent technology",
agent=researcher,
)
```
## Assembling Your Crew
Now let's put it all together:
```python
# Create the crew with our agents and tasks
crew = Crew(
agents=[researcher],
tasks=[research_task],
process=Process.sequential,
verbose=True
)
# Kick off the work
result = crew.kickoff()
```
Run your code with the following command:
```bash
python main.py
```
## What Did We Just Do?
That's it! You've successfully created your first AI agent using CrewAI. You should see a full report of your task in the terminal.
You can further customize your agents, tasks, and crew as needed and add more complex workflows.
Another thing to note is that you can use Pydantic models to make sure you get consistent task outputs and agent responses. Watch this [tutorial](https://www.youtube.com/watch?v=dNpKQk5uxHw) for more details on how to use Pydantic models with CrewAI.
## Best Practices to Keep in Mind When Building with CrewAI
1. Give agents clear, specific roles and goals
2. Provide relevant context in task descriptions
3. Use appropriate tools for the task
4. Not all LLMs are created equal; for example, some are not suitable for tool calling
5. Use Pydantic models to ensure consistent task outputs and agent responses
Check out CrewAI [documentation](https://docs.crewai.com/) for more detailed information and advanced usage examples.
**PS**: _I manage the docs for CrewAI. If you have any questions or feedback, don't hesitate to reach out to me on [Twitter](https://twitter.com/tonykipkemboi)._
---
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Last updated: 2026-01-27T22:07:32.760Z