AI Workflows & Agentic Architecture Directory

Build a click fraud detection agent workflow with LangGraph: real-time bot traffic detection, ad campaign monitoring, and automated fraud response using Google Ads API.

Build a Math Research Agent workflow with LangGraph: AI-assisted theorem proving with Lean 4 formal verification, literature contextualization, and community attribution.

Build an automated SEO agent workflow with LangGraph: continuous search performance monitoring from Search Console, PageSpeed analysis, structured data validation, and automated optimization.

Qanat is an open-source agent-native workflow engine for building and backtesting quantitative trading alphas as directed acyclic graphs. Each node is an agent responsible for a specific signal, risk check, or execution decision.

OKF (Open Knowledge Format) v0.2 is the fastest-growing agent memory standard in 2026 with 597 GitHub stars in its first week. It implements git-native persistent memory with sub-300us BM25 search -- letting AI coding agents retain context across sessions, branches, and team members without a vector database.

Obra Superpowers has exploded to 285K GitHub stars by rethinking how AI agents collaborate on software — not as monolithic coding tools but as a skills-driven ecosystem of specialized sub-agents. This guide builds a production LangGraph workflow that mirrors the Obra methodology: orchestrating planning, coding, reviewing, and testing agents with failover, memory, and self-healing loops.

Build a peerd browser-based AI agent harness where agents run entirely in-browser using WebGPU-accelerated local LLMs. LangGraph orchestrates agent state with IndexedDB persistence, zero server backend required.

Build a SimCity agent workflow where AI agents play simulation games through a REST API. LangGraph orchestrates autonomous city-building, resource management, disaster response, and economic optimization — all through structured agent tool calls to a game API.

Build a deterministic agent testing workflow using Spec27 spec-driven validation and LangGraph. Achieve 42% lower defect rates in production AI agents with property-based testing, automated evaluation harnesses, and regression guardrails.

Build a memory-aware agent workflow for Cursor IDE using MCP preferences to persist coding preferences, project conventions, and learned developer patterns across sessions. Inspired by Cursor's 109-point HN-released MCP memory feature.

Build a multi-agent orchestration hub where every agent registers as an MCP server. LangGraph discovers agents via MCP protocol handshakes, routes tool calls between agents, and enables deterministic inter-agent communication without custom integration code.

OneCLI's 88-point YC S26 sandboxed agent harness keeps SSH keys, API tokens, and DB credentials out of AI tool contexts. Build the full credential gateway: secret scanner, Docker sandbox, and team allowlist config.