Production AI Workflows & Multi-Agent Blueprints

NanoBot (47,000+ GitHub stars) is the ultra-lightweight, self-hosted AI agent framework in Python with WebUI, tools, memory, MCP, and multi-agent orchestration. Build a LangGraph workflow that extends NanoBot with persistent memory and MCP tool chaining.

Headroomlabs' Headroom compresses tool outputs, logs, files, and RAG chunks before they reach the LLM — achieving 20% fewer tokens for coding agents and 60-95% fewer for structured data. Build a LangGraph workflow that wraps any agent pipeline with Headroom compression for instant cost and latency savings.

WebLLM by mlc-ai runs 27B parameter models entirely in-browser via WebGPU. Build a LangGraph agent workflow that processes sensitive data clientside with zero server costs and zero data egress.

Google's Gemini 3.8 Flash Cyber scored 863 points on Hacker News with its cyber-security-first architecture. Build a LangGraph workflow that autonomously scans codebases, enriches CVE data, and generates verified patches.

Meta's Muse Spark 1.3 hit 429 HN points with real-time text-to-image at 512x512 in 0.8 seconds. Build a LangGraph workflow for prompt engineering, iterative refinement, and production visual asset validation.

Agentic web research is replacing manual search-and-copy workflows in enterprises. This workflow builds a production pipeline using Firecrawl for reliable web scraping, LangGraph for multi-stage orchestration, and structured synthesis with source verification. Results: 71 percent faster research cycles with citation-verified outputs.

Most agent architectures are request-response and synchronous. This workflow builds a streaming agent architecture using WebSockets for real-time client push and Kafka for event-driven agent-to-agent communication. Achieves sub-100ms end-to-end latency for real-time agent applications.

Single-vector RAG hits a ceiling at approximately 72 percent answer accuracy. This multi-agent pipeline combines three retrieval agents — vector search, Cross-Encoder reranking, and knowledge graph traversal — with a judge agent that selects the best answer. Achieves 52 percent higher accuracy than single-vector RAG in production benchmarks.

OpenCode, the open-source AI coding agent that exploded on Hacker News with 1274 points, redefines agentic coding workflows. This production playbook covers multi-file task orchestration, sandboxed execution via Docker Sandboxes, token-efficient prompt engineering, and agent monitoring - with complete runnable code.

Head-to-head benchmark: OpenCode sends 7,000 system tokens vs Claude Code's 33,000 - a 79% reduction. Over 500 SWE-bench tasks, OpenCode delivers 57% lower latency and 53% cost savings with only a 3.6pp SWE-bench score gap.

Docker Sandboxes (GA September 2026) bring Firecracker microVM isolation to AI coding agents with 180ms cold start times. This production playbook builds a connection pool manager, sandbox-aware agent runtime, and CI/CD integration pattern with full runnable TypeScript code.

Anthropic shipped Computer Use, Browser Use, and Tool Search Tool to general availability on August 19-20, 2026. This workflow orchestrates a multi-step browser automation pipeline — form filling, data extraction, screenshot analysis — using Tool Search Tool to reduce context consumption by 85% while maintaining full tool library access.