What we learned shipping a YAML-first agent runtime, the multi-agent patterns that hold up in production, the trade-offs of self-hosting your own coding assistant, and the things we got wrong before we got them right.
The hooks that actually work right now in the runtime, with the YAML to copy. Lint after every write, rate-limit a tool, hard-cap tool-call counts, run a custom Python validator on tool results.
We've shipped on both. LangChain wins on community size and Python ecosystem reach. Digitorn wins on iteration speed, audit, and shipping non-trivial multi-agent apps without writing framework glue. Here's the breakdown, with code.
A walkthrough of ten very different apps, all built declaratively on Digitorn. Coding agents, live React sandboxes, Slack bots, cron reporters, voice copilots. Same runtime, same shape, no Python.
A real cost breakdown from running a Claude Code clone over 50 sessions, the diagnosis we missed at first, and the four routing rules that ended up saving the most money. With charts.
Digitorn's app YAML used to scatter related fields across the file. We rewrote the schema into eight canonical top-level blocks, each with one job. Here is what changed, why, and how the migration tool keeps every old YAML working.
We started with Python, like everyone else. Then we hit hot-reload, audit, and on-call review, and kept hitting them until something had to change. This is the story of why Digitorn ships YAML-first, what we gave up, and what we gained.
AI agents explained without the hype. The 4 ingredients, multi-agent patterns, real YAML examples, and how to build one in 10 minutes - with diagrams and source code.
Self-host your own Claude Code in 50 lines of YAML. Same tools, same multi-agent loop, your API keys. With the actual config and a 5-minute install.
Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.