Production-ready blueprints for building with AI agents
Practical, copy-ready blueprints for AI agents, automation, model routing and MCP. Every blueprint has concrete steps, a config, code or prompt you can copy, and honest cost, setup time and failure modes.
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Blueprints added or re-verified against official docs in the 7 days to 2026-10-23.
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Start from the problem you are solving.
AI Agent Workflows
Architectures and control loops for reliable AI agents.
n8n Automation Blueprints
Importable n8n workflow patterns using AI nodes.
Multi-Model Routing
Gateways, fallbacks, caching and cost-aware model routing.
Prompt & Agent Patterns
Proven prompt structures and defensive patterns.
MCP & Tool Use
Model Context Protocol servers, configs and tool design.
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AI Lead Follow-Up and Appointment Booking Agent (GoHighLevel)
Text every new lead within minutes, let a Conversation AI bot qualify them and book a call on your calendar, then hand off to a human, all inside GoHighLevel.
Agent Tracing and Evaluation Setup
Capture structured traces of every LLM and tool call and run a small regression eval suite on each change.
Citation RAG on Qdrant (Self-Hosted Vector Store)
Run the grounded-citations RAG pattern on a local Qdrant container: stable chunk IDs, payload-filtered retrieval, and answers that cite only the chunks Qdrant returned.
Evaluator-Optimizer Refinement Loop
One model generates, another grades against a rubric, and the loop repeats until the rubric passes or a limit is hit.
Human-in-the-Loop Approval Gate
Let agents draft freely but require explicit human approval before any irreversible or external action.
Langfuse Tracing for OpenAI Agents
Drop in Langfuse's OpenAI wrapper to capture prompts, completions, latency, and token usage for every agent LLM call — then nest tool steps under one trace.
Missed-Call Text-Back Agent (GoHighLevel)
Automatically text callers you miss, with a booking link or an AI bot that picks up the conversation, using GoHighLevel's phone system and workflows.
OpenAI Realtime Voice Agent (Browser WebRTC)
Build a speech-to-speech voice agent in the browser with OpenAI's Realtime API, ephemeral client secrets, and the Agents SDK RealtimeSession.
Planner-Executor Agent
Split a task into an explicit plan, execute steps one at a time, and re-plan only when a step fails.
RAG Agent with Grounded Citations
Retrieval-augmented answering that cites source chunks and refuses when the context does not support an answer.
ReAct Tool-Calling Agent Loop
A minimal, provider-agnostic reason-act-observe loop with a step budget, tool registry, and safe termination.
Supervisor Multi-Agent Router
A supervisor agent delegates sub-tasks to specialist agents with narrow tools and merges their outputs.
n8n Form Lead Enrichment and CRM Sync
Capture a lead via n8n Form Trigger, research the company website with an LLM, and upsert into a sheet or CRM.
n8n Global Error Alerting Workflow
A dedicated error workflow that alerts you with the failing workflow, node, and execution link whenever any workflow fails.
n8n Inbox Triage with LLM Classification
Classify incoming email into categories with structured output, then label, route, or draft replies.
n8n RSS to AI Summary Digest
Daily schedule pulls RSS feeds, summarizes new items with an LLM, and posts a digest to Slack or email.
n8n Webhook-Triggered AI Agent
Expose an n8n AI Agent (with memory and tools) behind a webhook that returns the answer synchronously.
Agent Cost Monitoring with LiteLLM Budgets and Spend Logs
Track token spend per key, user, and team on a LiteLLM proxy, enforce max_budget caps, and pull spend reports — distinct from tracing/evals.
Complexity-Based Cheap/Strong Model Router
Route easy requests to a cheap model and hard ones to a strong model using a lightweight classifier plus escalation.
LiteLLM Proxy with Fallbacks and Load Balancing
An OpenAI-compatible gateway that load-balances deployments and falls back across providers on errors.
Local-First Routing with Ollama and Cloud Fallback
Serve requests from a local Ollama model, falling back to a cloud model only when local fails or the task needs it.
OpenRouter Model Fallback and Provider Ordering
Use OpenRouter's request-level models array and provider preferences for automatic failover.
Semantic Response Cache in Front of LLM Calls
Reuse prior answers for near-duplicate prompts using embedding similarity with a strict threshold and TTL.
Chain-of-Verification Fact Check Prompt
Draft an answer, generate verification questions, answer them independently, then revise, reducing factual errors.
Few-Shot Classification Prompt
A compact few-shot prompt for labeling text with balanced examples, an explicit label set, and an escape label.
Production Agent System Prompt Template
A sectioned system prompt template covering role, context, tools, rules, and output format for tool-using agents.
Prompt Injection Defense for Tool-Using Agents
Layered mitigations for agents that read untrusted content such as web pages, emails, and documents.
Reliable Structured JSON Output Pattern
Get valid, schema-conforming JSON from any model using native structured outputs where available, and validate-and-repair otherwise.
Browser Automation Agent with Playwright MCP
Give an MCP client a Playwright-powered browser so the agent can navigate, click, and fill forms using accessibility snapshots instead of screenshots.
Build a Custom MCP Server in Python (MCPServer, formerly FastMCP)
Expose your own functions as MCP tools and resources using the official Python SDK in about 20 lines.
Build a Custom MCP Server in TypeScript
A minimal stdio MCP server using @modelcontextprotocol/sdk with a zod-validated tool.
GitHub MCP Server with a Scoped Token
Run GitHub's official MCP server in Docker with a fine-grained, least-privilege personal access token.
MCP Starter Config for Claude Desktop
A claude_desktop_config.json wiring up filesystem, fetch, and git MCP servers with scoped access.
Read-Only Database Access for Agents via MCP
Give an agent safe SQL querying through an MCP server connected with a read-only database role.
Tool Design Checklist for Agents and MCP
How to name, describe, and shape tools so models call them correctly and safely.
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