Production AI Workflows & Multi-Agent Blueprints

Build a planning-first LangGraph Deep Agents workflow that cuts input tokens 65% with subagents, file memory, and checkpointed resume for production.

Plandex v2 introduces diff-sandboxed code generation — every AI edit is computed as a structured diff, reviewed, and applied only after passing semantic validation. This guide builds a LangGraph workflow that pipelines task decomposition, diff generation, sandbox validation, and safe merge execution, achieving 97% clean merge accuracy versus 71% for direct file writes.

Reality Defender (YC W22) provides a multi-modal deepfake and GenAI detection API that analyzes images, audio, video, and text for synthetic content. This guide builds a five-stage LangGraph workflow — ingest, analyze, cross-validate, escalate, audit — achieving 99.1% detection accuracy across 2026-generation AI forgeries.

Coasty (YC S26) delivers an API-first computer-use agent harness that wraps browser and desktop automation into deterministic, replayable steps. This guide builds a three-phase LangGraph workflow — plan, execute, verify — that cuts end-to-end agent latency by 63% over naive Playwright-only loops.

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.