# This NEW AI AGENT Builder Update is INSANE! 🤯

## Метаданные

- **Канал:** Julian Goldie SEO
- **YouTube:** https://www.youtube.com/watch?v=tAR4BKUTf1M
- **Дата:** 11.01.2026
- **Длительность:** 8:03
- **Просмотры:** 573
- **Источник:** https://ekstraktznaniy.ru/video/10049

## Описание

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Letta Code: The AI Agent That Actually Remembers

Discover Letter Code, an open-source framework for building AI agents with persistent memory that learn and grow with your business. Stop starting from scratch every session and build a stateful assistant that masters your code, style, and workflows over time.

00:00 - Intro: The AI with Memory
00:49 - The Problem with Amnesiac AI
01:14 - What is Letter Code?
02:15 - Skill Learning & Reusable Expertise
02:51 - How to Setup Your Agent
04:09 - Open Source & Search Features
05:44 - Getting Tactical: Your First Agent
07:28 - Join the AI Community

## Транскрипт

### Intro: The AI with Memory []

There's this brand new AI agent builder that just dropped and it's completely different from everything else out there. It actually remembers what you taught it. Like a real assistant that gets better over time. I'm talking memory. I'm talking skills. I'm talking open source. This is Letter Code and it's about to change how you work with AI forever. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment. So, make sure you comment below. All right, so here's the thing. Every AI tool you've used so far forgets everything. After each session, you close the chat, it's gone. You start fresh every single time. It's like hiring an assistant who gets amnesia every day. But letter code

### The Problem with Amnesiac AI [0:49]

is different. This thing actually remembers. It learns. It gets smarter the more you use it. Let me explain what letter is first. Letter is an open-source framework for building AI agents that have persistent memory. That means they don't forget. They store information about you, your projects, your preferences, everything. Uh, and they use that information to help you better over time. Think about it like working with a human assistant. Day one, they're learning. Day 30, they know exactly how you work. Now, Letter Code

### What is Letter Code? [1:14]

is the latest update. This is specifically designed for developers and anyone building stuff with AI. It's a memory first coding agent. This agent learns your codebase, learns your patterns, learns your style, and it keeps all of that in memory. So, it gets better and better. Here's what makes this crazy. Most coding assistants are stateless. You ask them to write code, they write it, done. Uh, next time they have no idea what happened before, right? But letter code persists. You initialize an agent with a command like /init. That agent studies your project. It builds memory blocks. It understands your structure. And from that point on, every interaction builds on the last one. Let me give you a real example. Say you're building automation systems for the AI profit boardroom. You need to create member dashboards, set up email sequences, build landing pages. With normal AI, you'd explain the whole context every single time. But with letter code, you teach it once. It remembers your brand guidelines. It remembers your text stack. It remembers how you like things structured. Then every new task builds on that knowledge. And here's where it gets even better.

### Skill Learning & Reusable Expertise [2:15]

Letter code has something called skill learning. When you teach it to do a repeated task like creating a new dashboard page or setting up a database schema, it can save that as a skill, a markdown file that captures the whole process. Then you can reuse that skill across projects. You can even share it with other agents. It's like building a library of expertise that grows over time. The architecture is really smart, too. You've got memory blocks that the agent actively manages. It can insert new information, replace old information, rethink its understanding. It's not just storing data. It's organizing and updating its knowledge like a real person would. Now, let's

### How to Setup Your Agent [2:51]

talk about how this actually works. First, you install the client. You can use mpm or pip depending on your language. Then, you create an agent and define its memory blocks. Maybe you give it context about your business, your target audience, your goals. For the AI profit boardroom, you might tell it about automation use cases, community structure, content themes. All of that goes into memory. Then you give it tools. Tools are actions the agent can take. search tools, API calls, system commands, whatever you need. The agent uses these tools to actually do things, not just talk about them. And here's the key part. The agent separates its reasoning from its communication. You can see it thinking through problems step by step. That transparency is huge for debugging. Let me show you what this looks like in practice. Say you're creating content for your AI community. You initialize a letter code agent for your content workflow. You teach it your content structure, your voice, your key topics. The agent remembers all of this. Now, every time you need a new video script or blog post, it already knows your style. audience. And this works across sessions. You can close everything, come back 3 days later, and the agent still remembers. It picks up right where you left off. That's the power of persistent memory. It's not just convenient. It actually makes the agent more effective over time because it's building on accumulated knowledge. Now, here's

### Open Source & Search Features [4:09]

something really cool. Letter code has builtin search functionality. You can use commands like slash search to query past interactions, code histories, anything in memory. So if you taught the agent something 6 weeks ago and you forgot the details, you just search for it. The agent pulls it up instantly. The fact that this is open source is massive, too. You're not locked into one company's model or one company's platform. Letter works with OpenAI, Anthropic, Google, Gemini, whatever you want. You can switch models. You can customize everything. And on benchmarks like Terminal Bench, Letter Code is the top performer among open-source coding agent harnesses. And if you want to learn how to save time and automate your business with AI tools like Letter Code, you need to check out the AI Profit Boardroom. This is where we break down exactly how to use cuttingedge AI agents, automation tools, and workflows to build real systems for your business. No fluff, just practical implementation. I'll drop the link in the description. Back to Letter Code. One of the coolest features is the agent file format. That's a file that captures your entire agent memory, tools, state, everything. You can export it, share it, import it into a new environment. Imagine building an agent that's perfectly trained for your business workflow. Now, you can package that up and deploy it anywhere. The agent development environment deserves attention, too. This is not just a chat interface. This is a full development platform. You can see exactly what's in the agents memory at any moment. You can watch it reason through problems. You can test different tools and see the results in real time. For anyone building serious AI systems, this level of visibility and control is gamechanging. Now, let's get tactical.

### Getting Tactical: Your First Agent [5:44]

How do you actually start using this? First, go to the Letter website. That's letter. com. You'll find the documentation, the GitHub repo, everything you need. Install the client for your preferred language. Python and TypeScript are both supported. Then follow the quick start guide to create your first agent. Start simple. Give it one clear task and let it learn. As you work with the agent, pay attention to what it's remembering and how it's organizing information. You can actually edit the memory blocks manually if you need to. This gives you fine control over what the agent knows. Over time, you'll develop intuition for how to structure memory for maximum effectiveness. The skill learning feature is where things get really powerful. Whenever you find yourself doing the same type of task multiple times, teach it to the agent as a skill. Document the process, give examples, let the agent practice. Once it's locked in as a skill, you never have to explain it again. For anyone building content systems, marketing automation, community management, customer support, this technology is a breakthrough. You're not just automating repetitive tasks. You're building an AI assistant that actually understands your business and gets better at helping you over time. The move from stateless to stateful AI agents is one of the biggest shifts in how we work with AI for the first time. We have systems that genuinely learn from experience and carry that learning forward. Letter Code is leading that shift and is completely open for anyone to use. So, here's what you should do. Go check out the Letter documentation, install Letter Code, and create your first agent. Start with something simple in your workflow and let the agent learn it. Watch how it builds memory and improves over time. Experiment with skills, test the search functionality, explore the tools, and if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI success

### Join the AI Community [7:28]

lab. That's our free AI community. Links in the comments and description. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. Real people, real implementations, real results. The future of AI assistance is not just smarter models. It's agents that remember, learn, and evolve with you. Letter Code is proving that future is already here. Open source, model agnostic, production ready. This is the kind of tool that changes how you work. Check it out. Let me know what you think in the comments and I'll see you in the next