Use Typed Wrappers And Codecs

SkillDev tools

Guides your agent to add typed wrappers and codecs in NeMo Relay while keeping middleware JSON behavior unchanged.

Then ask your AI: use the Use Typed Wrappers And Codecs skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.

About this skill

Use this skill when adding NeMo Relay typed wrappers, domain types, or provider codecs while preserving JSON middleware semantics and caller-visible behavior.

What this skill tells your AI

The instructions your AI receives, as published by nvidia/skills in skills/nemo-relay-instrument-typed-wrappers/SKILL.md and read by Ahel’s review.

Use this skill when an application wants stronger domain types than raw JSON for tool or LLM integration. Keep typed boundaries explicit so middleware still sees predictable JSON.

Default Guidance

  • Prefer plain JSON first for initial adoption.
  • Reach for typed wrappers when the application already has stable domain models.
  • Keep in mind that middleware still operates on JSON, not typed objects.

Embedded Codec Model

  • A typed value codec is a pure boundary translator. It converts application-facing values to JSON before NeMo Relay emits events or runs middleware, then converts JSON back into the framework callback or caller type.
  • Python exposes JsonPassthrough, DataclassCodec, PydanticCodec, and BestEffortAnyCodec. Node.js exposes JsonPassthrough plus custom Codec<T> implementations.
  • Use BestEffortAnyCodec only at boundaries where strict schemas are not available. Prefer dataclass, Pydantic, or explicit Node.js codecs when the framework owns a stable schema.
  • Provider codecs are different from typed value codecs: they normalize provider-specific LLM requests and responses so middleware and subscribers can inspect messages, tools, model names, generation parameters, and response annotations.
  • Built-in provider codecs include OpenAIChatCodec, OpenAIResponsesCodec, and AnthropicMessagesCodec in Python, Node.js, and Rust. Choose the codec that matches the actual provider payload shape.
  • Response codecs annotate LLM end events with fields such as id, model, message, tool_calls, finish_reason, usage, provider-specific data, and extra unmodeled fields. They do not rewrite the caller-visible response.
  • Request codecs run before LLM request intercepts. Intercepts receive both the raw LLMRequest and optional annotated request; encode merges annotated edits back before execution intercepts and the provider callback run.

Key Rules

  • Typed wrappers are currently a first-class path for Python and Node.js; Rust uses codec traits directly
  • Request/response conversion belongs in codecs
  • Intercepts and guardrails see JSON values after encoding
  • Changes made by middleware survive into the decode step

Choose A Codec

  • JsonPassthrough for JSON-native values
  • DataclassCodec or PydanticCodec in Python when the models already exist
  • Custom codecs for domain-specific wire shapes
  • BestEffortAnyCodec only when broad flexibility is worth the looser contract
  • Provider codecs for LLM provider payloads, not application domain objects conversion

Validation Checklist

  • Codec output is JSON-compatible
  • Required fields survive toJson/fromJson or decode/encode
  • Middleware sees the expected serialized shape
  • Provider codecs preserve fields they do not understand
  • Response codec failures do not break the underlying LLM call
  • Request codec encode preserves original provider fields unless an intercept intentionally changes them

Related Skills

  • nemo-relay-instrument-calls
  • nemo-relay-plugin-observability
  • nemo-relay-debug-runtime-integration

Signals

GitHub stars
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Last commit
Oct 2026
Advanced
Item type
skill
Key
nemo-relay-instrument-typed-wrappers
Source
github.com/nvidia/skills