What you get Typed LLM calls — output schema is a language type; violations trigger automatic repair, never reach your code Effect system — pure / LLM / Tool / IO effects inferred and shown in signatures Deterministic replay — full, hybrid, and live modes; traces are git-friendly JSONL Budget contracts — per-call, per-run, and per-repair USD ceilings (NUDGE_REPAIR_BUDGET) with static estimation (nudgec cost) Checkpointed agent state — crash, then nudge resume from the last checkpoint Native parallelism — par map, par race, par all with compile-time race safety Prompt Clippy — the compiler lints your llm""" blocks: vague instructions, missing output contracts, overlong prompts MCP & Python interop — consume real MCP servers over stdio as typed tools; escape to any pip package Real providers — one stdlib-only adapter for OpenAI / Gemini / Groq / MiMo / Mistral / Anthropic / Ollama; free tiers and local models work at $0 Trace viewer — nudgec trace-view opens a local web UI over any run: timeline, tokens, cost, repairs highlighted, par lanes color-coded (NTF v1.1 branch field) Trace diff — nudgec trace-diff a.jsonl b.jsonl answers "what changed when I edited the prompt?": totals and per-record deltas A2A agent-card export, LSP, OpenTelemetry — built in, not bolted on

GitHub

AI,LLM,Programing-langue,a2a

AI,LLM,Programing-langue,a2a