Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Pydantic quietly dropped the most underrated framework for building Agents with MCP and tool calling. Learn how to run it in just 3 mintues.

matt palmer

38,057 subscribers

84,917 views • 1 year ago •via X (Twitter)

Science & Technology News & Politics Education

Anya Rossi• Live Now

Private livecam show

10 Comments

matt palmer1 year ago

1️⃣ Remix the replit template 2️⃣ Paste in your API key 3️⃣ Click "Run" to see how it works 4️⃣ Deploy > Scheduled > enter time in natural language 5️⃣ Click "Deploy"

matt palmer1 year ago

Youtube here: Remix the template to get started

Samuel Colvin1 year ago

Hey, thanks so much for the kind words about Pydantic AI, I would love to talk about collaborating more, can I DM you? Or email me ([email protected]) if it's easier.

dennis hegstad1 year ago

👀

Michael S1 year ago

Does pydantic handle closing the mcp connection automatically??

Jesse1 year ago

Deadly!

Mr_C1 year ago

Amazing can’t wait to test this out this week vibes coding for me is all about utility and using the power of MCP !! Thanks Mattt

LabelGuy1 year ago

Love these info packed ultra-quick videos. Thanks @mattppal !

D3conomist 🍉 🍉 🍉1 year ago

wow... you replitians just never dissapoint. Thanks for this good ser!

Waldo1 year ago

Thank you so much for sharing your knowledge, Matt! I really enjoy your tutorials 💯💯💯

Related Videos

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.

Unwind AI

79,540 views • 1 year ago

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5

AshutoshShrivastava

130,272 views • 1 year ago

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

joyce

14,133 views • 4 months ago

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 views • 1 year ago

“People aren’t just building for humans anymore. They’re building for agents.” Cloudflare shares how Cloudflare Sandbox SDK works with the OpenAI Agents SDK to help agents run code in secure environments while keeping sensitive data separate from execution.

“People aren’t just building for humans anymore. They’re building for agents.” Cloudflare shares how Cloudflare Sandbox SDK works with the OpenAI Agents SDK to help agents run code in secure environments while keeping sensitive data separate from execution.

OpenAI Developers

40,871 views • 2 months ago

Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.

Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.

matt palmer

64,690 views • 1 year ago

Most AI agents are built to replace creatives, we built ours to stay by your side For the first time, what Agents learn is shared across your team. You direct, watch the process in real time, and edit it live Agents + MCP + Flows, just launched at Upscale Conf SF

Most AI agents are built to replace creatives, we built ours to stay by your side For the first time, what Agents learn is shared across your team. You direct, watch the process in real time, and edit it live Agents + MCP + Flows, just launched at Upscale Conf SF

Magnific

825,449 views • 1 month ago

How do AI agents actually take action? First, they have to see. Cognitive scientist Dr. Danielle Perszyk and AI researcher Matthew Elkherj dive into one of the most underestimated challenges in building AI agents: perception. Episode 3 of Making a Mind explores how agents learn to interpret the web—and why it matters. 🎧

How do AI agents actually take action? First, they have to see. Cognitive scientist Dr. Danielle Perszyk and AI researcher Matthew Elkherj dive into one of the most underestimated challenges in building AI agents: perception. Episode 3 of Making a Mind explores how agents learn to interpret the web—and why it matters. 🎧

Amazon News

25,217 views • 5 months ago

run AI agents in one line of CLI command 🤯 you can run tool-calling AI agents or web automation agents through CLI in smolagents get started as easily as $ webagent {prompt} in CLI tool-calling agent and more details on the next one ⬇️

run AI agents in one line of CLI command 🤯 you can run tool-calling AI agents or web automation agents through CLI in smolagents get started as easily as $ webagent {prompt} in CLI tool-calling agent and more details on the next one ⬇️

merve

35,905 views • 1 year ago

Pydantic AI Agents: NEW Multi-Agent Framework 📚 Step-by-step Hands-on Tutorial 🔍 How to Install PydanticAI? 🛠️ What Models are Supported? Used Groq Inc 🎯 How to Create First Agent? ⚡ How to Add Custom Tools? 🔌 Built-in validation 🎮 How it differs from CrewAI or AutoGen? Pydantic Samuel Colvin jason liu

Pydantic AI Agents: NEW Multi-Agent Framework 📚 Step-by-step Hands-on Tutorial 🔍 How to Install PydanticAI? 🛠️ What Models are Supported? Used Groq Inc 🎯 How to Create First Agent? ⚡ How to Add Custom Tools? 🔌 Built-in validation 🎮 How it differs from CrewAI or AutoGen? Pydantic Samuel Colvin jason liu

Mervin Praison

26,723 views • 1 year ago

Anthropic engineers just dropped 40 minutes on how they actually build loops with Claude Code. Not prompts. Not tools. Loops. 3 agents. 1 loop. One plans. One builds. One judges. Plan → Build → Judge → Repeat. The agent doesn't stop until the app works. Most people are building agents that run once and hope for the best. The best teams are building agents that cycle, self-correct, and improve with every iteration. That's the gap. Watch it, then read this before you build your next loop.

Anthropic engineers just dropped 40 minutes on how they actually build loops with Claude Code. Not prompts. Not tools. Loops. 3 agents. 1 loop. One plans. One builds. One judges. Plan → Build → Judge → Repeat. The agent doesn't stop until the app works. Most people are building agents that run once and hope for the best. The best teams are building agents that cycle, self-correct, and improve with every iteration. That's the gap. Watch it, then read this before you build your next loop.

Rahul

60,310 views • 8 days ago

Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

Bindu Reddy

13,598 views • 1 year ago

Introducing Tests for ElevenLabs Agents. Run built-in test scenarios to improve the success rate of your agents across: - Tool calling - Human transfers - Complex workflows - Guardrails - Knowledge retrieval

Introducing Tests for ElevenLabs Agents. Run built-in test scenarios to improve the success rate of your agents across: - Tool calling - Human transfers - Complex workflows - Guardrails - Knowledge retrieval

ElevenLabs

49,920 views • 10 months ago

Calling for violence in Charlie Kirk’s name goes against everything he stood for. Pick up the torch he dropped and fucking run with it. Thats how you honor him.

Calling for violence in Charlie Kirk’s name goes against everything he stood for. Pick up the torch he dropped and fucking run with it. Thats how you honor him.

The Misfit Patriot

12,550 views • 10 months ago

Totally settling the "MCP vs CLI for coding agents" debate with this humongous blog post. I benchmarked screen, tmux, and a bespoke custom CLI and MCP tool against 3 task that require the agent to steer TUIs (lldb, python repl, opencode)

Totally settling the "MCP vs CLI for coding agents" debate with this humongous blog post. I benchmarked screen, tmux, and a bespoke custom CLI and MCP tool against 3 task that require the agent to steer TUIs (lldb, python repl, opencode)

Mario Zechner

65,025 views • 10 months ago

Superior Agents are the most powerful AI agents on the market. They make decisions without human input, learn from real-world experience, and evolve over time. Powered by the open-source Superior Agents framework. If you’re building intelligent agents, start here.

Superior Agents are the most powerful AI agents on the market. They make decisions without human input, learn from real-world experience, and evolve over time. Powered by the open-source Superior Agents framework. If you’re building intelligent agents, start here.

KIP Protocol

125,823 views • 1 year ago

Today we’re excited to introduce GenSX, an open source TypeScript framework for building backend workflows and agents with reusable, React-like components. If you know how to write a React component, then you’ll feel right at home building agents in GenSX.

Today we’re excited to introduce GenSX, an open source TypeScript framework for building backend workflows and agents with reusable, React-like components. If you know how to write a React component, then you’ll feel right at home building agents in GenSX.

Evan Boyle

14,446 views • 1 year ago

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Akshay 🚀

286,050 views • 8 months ago