I got tired of parsing LLM output by hand, so I wrote a programming language for agents

NEKOMYA — Wed, 29 Jul 2026 07:33:41 +0000

Production agents in 2026 are still held together with Python glue: prompt chains parsed by hand, tool calls wrapped in try/except, no replay, no cost control, no regression tests. Libraries patch symptoms. I wanted to fix the layer where the problem actually lives — the language.

So I built Nudge: a typed, replayable, budget-aware programming language for LLM agents. The compiler is written in Rust (zero dependencies), and it compiles to Python & TypeScript.

The pain, concretely

Pain	With libraries	With Nudge
Untyped LLM output	validate at runtime	schema is a type — proven at compile time
Hidden side effects	invisible	`uses LLM, Tool, IO` in every signature
No regression testing	record/replay bolted on	every run emits a trace; every trace is a test
Cost surprises	dashboards after the fact	budget is a contract, enforced by compiler + runtime

A taste

type Finding = { claim: string, source: Url, confidence: float @range(0, 1) }

fn analyze(q: string, hits: [SearchResult]) -> [Finding] uses LLM {
    llm"""Extract verifiable findings about {q} from: {hits}"""
    with { schema: [Finding], model: "anthropic:sonnet-4.6",
           budget: 0.03 USD, retry: 2 with repair }
}

test "stays within budget on recorded trace" {
    let t = replay("traces/demo.jsonl")
    assert t.cost_usd < 0.25   // zero tokens burned in CI
}

The compiler proves the schema matches, infers effects, and computes a static cost bound. The runtime records every call to a content-addressed trace you can diff, commit, and replay.

What you get out of one .ndg file

Typed LLM calls — output schema is a language type; violations trigger automatic repair
Effect system — pure / LLM / Tool / IO effects shown in signatures
Deterministic replay — traces are git-friendly JSONL; replay tests burn zero tokens
Budget contracts — per-call and per-run USD ceilings with static estimation
Checkpoint/resume — crash, then nudge resume from the last checkpoint
Native parallelism — par map, par race, par all with compile-time race safety
MCP tools, A2A agent-card export, LSP, OpenTelemetry — built in, not bolted on

Try it

Everything runs against a deterministic fake provider by default — no API key, no token spend:

cargo build
export PYTHONPATH=$PWD/runtime
nudgec check examples/research_agent.ndg
nudgec cost examples/research_agent.ndg
cd examples && nudgec test research_agent.ndg

There's also a VS Code extension (search "Nudge Language" in the Marketplace) with an LSP for diagnostics, hover, and completion.

The project is MIT-licensed and v1.0 just shipped. I'd genuinely love feedback from people building agents in production — what would you want a language like this to prove for you?

GitHub: https://github.com/NekomyaDev/nudge

If you find it useful, a ⭐ on the repo means a lot at this stage. Thanks for reading!

DEV Community: NEKOMYA

I got tired of parsing LLM output by hand, so I wrote a programming language for agents

The pain, concretely

A taste

What you get out of one .ndg file

Try it