Framework AI Agent Skills

Beelzebub is an advanced honeypot framework designed to provide a highly secure environment for detecting and analyzing cyber attacks. It offers a low code approach for easy implementation and uses AI to mimic the behavior of a high-interaction honeypot. - Global Threat Intelligence Community - Key Features - Architecture - Quick Start - Configuration - Core Configuration - Service Configuration -

Type-safe agent framework from the Pydantic team. Structured outputs, dependency injection, and model-agnostic tool calls with Python type hints as the contract. First-class TypedDict validation of model output.

Design, audit, debug, and implement App Store-compliant mobile paywalls, subscription flows, pricing screens, trials, offers, feature gates, and subscription lifecycle UX. Use when the user asks to audit my paywall, fix App Store 3.1.2 rejection, improve trial-to-paid, choose plans/prices/trials, review RevenueCat/Adapty/Apphud/Superwall setup, calculate LTV/ROAS, or build paywall implementation.

Graph-based agent framework from LangChain. Build stateful multi-actor applications as directed graphs where nodes are agents or tools and edges encode control flow. The default for anything that needs branching or human-in-the-loop.

Microsoft's multi-agent conversation framework. Agents talk to each other in turns, human-in-the-loop optional. AutoGen Studio ships a visual builder on top. Widely used in research and enterprise pilots.

A next-generation framework documentation provider for Claude Code via Model Context Protocol (MCP). Provides query-focused API context by extracting TypeScript definitions directly from npm packages - delivering minimal, accurate information instead of dumping entire documentation pages. mcp-name: dev.augments/mcp Version 4.0 introduces a fundamentally new approach to framework documentation: Doc

OpenAI's official Python SDK for agents. Ships handoffs, guardrails, tracing, and a ReAct-style loop built around the Responses API. The reference for anyone starting on OpenAI models.

TypeScript framework for durable AI workflows and agents. Long-running tasks with automatic retries, checkpointing, and observability. Boundary case: also runs as a hosted platform, but the code is the entry point.

Role-playing multi-agent framework in Python. Define agents with roles, goals, and tools; assemble them into crews that collaborate on tasks. Widely adopted as the default "let me try multi-agent" starting point.

Build and evaluate agents with MCP, ACP, and A2A protocol support. Emphasis on the protocol layer so agents can talk to each other and to tool servers without bespoke plumbing.

TypeScript-first agent framework. Agents, workflows, tools, and evals in one library, aimed at devs building agents inside Next.js/Node apps rather than Python notebooks.

Python framework for building agents with memory, knowledge, and tools. Ships an agent playground UI and API server. Marketed as "5,000× faster than LangGraph" for hot-path agent instantiation.

Agent-native platform for stateful, durable agents on WebAssembly. Rust core with SDKs in multiple languages. Optimized for agents that need to survive process restarts and cluster failovers.