Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

Make Any Website & Tool Your CLI. A universal CLI Hub and AI-native runtime. Transform any website, Electron app, or local binary into a standardized command-line interface. Built for AI Agents to discover, learn, and execute tools seamlessly via a unified AGENT.md integration.

Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.

Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

0 - 1 learn OpenClaw: sections to build an claw-AI agent from scratch

Official Microsoft Learn MCP Server and CLI tool – powering LLMs and AI agents with real-time, trusted Microsoft docs & code samples.

An Open Source package that allows video game creators, AI researchers and hobbyists the opportunity to learn complex behaviors for their Non Player Characters or agents

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open-source framework for creating and managing simulations populated with AI-powered agents. It provides an intuitive platform for designing complex, interactive environments where agents can act, learn, and evolve.

autonomous trading agent powered by social sentiment analysis and ai that learns, grows, and adapts

A cache for AI agents to learn and replay complex behaviors.

AI Agent that learns how to play Snake with Deep Q-Learning

Lightweight, clear, and fully extensible AI agent infrastructure — learn easily, customize anything 🐉

Curated Agent Skills for Microsoft & Azure – giving AI coding assistants structured, real-time expertise from Microsoft Learn docs.

Low-Code Full-Stack Agentic AI Development using LLMs, n8n, Loveable, UXPilot, Supabase and MCP. Class Videos: https://www.youtube.com/playlist?list=PL0vKVrkG4hWq5T6yqCtUL7ol9rDuEyzBH

AI Legal Assistant skill for Claude Code. Contract review, risk analysis, NDA generation, compliance auditing, negotiation strategy, and PDF reports — 14 skills, 5 parallel agents. If you want to learn how to sell this to real businesses, check out the Skool community

Procedural memory for AI coding agents: transforms scattered session history into persistent, cross-agent memory so every agent learns from every other

AI agents that remember, automate, and learn new skills — work from desktop or Telegram, publish to the world.

Learn to build real-world applications with Claude Agent SDK. This tutorial takes you from basics to advanced implementations, covering essential integrations (Skills, MCP) and the core principles of human-AI interaction.

Build production-grade agents with OpenAI AgentKit, a no-code platfrom.

Runtime for building and managing AI agents and Workflows. Easy to learn, fast to build, High Performance, Reliable by design, Intuitive UI, Production Ready.

Cognitive memory for AI agents — learns from use, forgets what's irrelevant, strengthens what matters. Single binary, fully offline.

Master the art of designing and organizing AI agents. Learn to automate complex, multi-step business processes by creating specialized AI agent teams using the open-source library crewAI.

Learn Pydantic AI agents, step by step, using local models and ollama

Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain & CrewAI integrations.

Learn to build and customize multi-agent systems using the AutoGen. The course teaches you to implement complex AI applications through agent collaboration and advanced design patterns.

Learn how to build AI agents with PydanticAI

Learn production-grade AI agent development from Claude Code's architecture

Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).