AINL turns messy prompt loops and brittle multi-agent orchestration into clean, deterministic graphs. Define workflows in a tiny DSL → get compile-time safety, clean per-node context, and emit to LangGraph, Temporal, or any runtime. Built-in visualization, MCP integration for coding agents, and big token/energy savings.Human-initiated + co-developed with AI agents. https://github.com/sbhooley/ainativelang

AppSignal — Get full visibility into app health, errors, and performance

Get full visibility into app health, errors, and performance

Hey Product Hunt 👋 I'm the human who started AINL, and I've been building it together with AI agents (Cursor, Claude Code, etc.) from day one. After years of fighting ever-growing context windows, non-deterministic prompt chains, and brittle orchestration in multi-agent setups, I got tired of it. So I created a tiny DSL where you define workflows once, get strict compile-time validation (unreachable nodes, bad calls, contract violations), beautiful Mermaid graphs, and then emit the same source to LangGraph, Temporal, FastAPI, Docker/K8s — or just run it natively. "Compile once, run many" means way less token waste and much cleaner per-agent context. It also ships with an MCP server so your coding agents can `ainl-validate` and `ainl-compile` their own workflows. Human-initiated. AI-co-developed. Made for production agents. Would love your honest feedback — especially on the IR design, missing emitters, or how this fits (or doesn't) into your current agent stack. Try it here: https://github.com/sbhooley/aina... <-- Don't forget to start the repo please! Thanks for checking it out!

AppSignal — Get full visibility into app health, errors, and performance

Get full visibility into app health, errors, and performance

AINativeLang

Compile AI agent workflows to deterministic graphs

Compile AI agent workflows to deterministic graphs