118 tested guides on AI agents, n8n, RAG, and automation — newest first.
Redis Chat Memory gives an n8n AI agent conversation history that survives restarts and is shared across queue-mode workers. Here is how the Session Key, Context Window Length, and TTL fields work, what it costs, and when to pick Redis over Simple Memory or Postgres.
In July 2026 you can call five frontier models before lunch, at prices that range tenfold. More choice is not an easier choice. Here is a job-by-job map of which model to default to, with every price dated and a reminder to test on your own data.
A new frontier model lands every week, and rewriting your integration each time is a losing game. Because most providers now speak the OpenAI API format, swapping a model is usually a base-URL and key change. Here is the portability pattern, and where it breaks.
Computer-use agents, which read the screen and drive mouse and keyboard, went from demo to shipping feature in 2026, and Meta's Muse Spark 1.1 made it a headline capability. Here is what they automate, where they break, and the guardrails that keep them safe.
For two weeks GPT-5.6 was a limited preview most builders could not touch. On July 9, 2026, OpenAI made Sol, Terra, and Luna generally available across the API, Codex, and ChatGPT. Here is what changed for builders, and what to verify before you adopt it.
Anthropic gave paid Claude plans a taste of Fable 5, then set a deadline. On July 12, 2026 the included window closes and Fable 5 moves to prepaid usage credits at $10/$50 per 1M tokens. Here is the cost cliff, who it hits, and how to prepare before the switch.
On July 9, 2026, Meta put a frontier-class model behind a paid, self-serve API for the first time. Muse Spark 1.1 is agentic, handles computer use and parallel subagents, and undercuts GPT-5.6 and Fable 5 on price. An honest, dated read for builders.
Should you call a closed API like Claude or GPT, or self-host an open-weight model like DeepSeek or Qwen? Skip the hot takes. This is a four-axis framework (cost, quality, control, privacy) plus a decision flowchart, using dated June 2026 facts.
You keep seeing function calling, tool use, and MCP used as if they compete. They do not. Tools are the functions, function calling is the model mechanism that calls them, and MCP is the standard that shares them across clients. Here is how the three layers stack and which to
Cursor, Windsurf, and GitHub Copilot are the three names most teams weigh in 2026, but two changes broke the old rankings: Windsurf is now Devin Desktop, and Copilot moved to usage-based billing. Here is the honest price-and-fit comparison.
Your n8n canvas hit 40 nodes and you keep copy-pasting the same blocks. A sub-workflow is the fix: a workflow that starts with the When Executed by Another Workflow trigger, so others call it like a function. Here is how data crosses the boundary and when to extract.
Agent memory is not something the model has; it is which past turns your framework re-sends each call. This compares the four ai agent memory types, full buffer, window, summary, and vector-backed, what each costs in tokens, and which map to real n8n nodes or patterns you wire.
