Adaptive

This page is for NeMo Relay, version v0.8.4. For other versions, use one of these documentation indexes: - Latest (v0.10.0) (default): https://docs.nvidia.com/nemo/relay/latest/llms.txt - dev: https://docs.nvidia.com/nemo/relay/dev/llms.txt - v0.10.0: https://docs.nvidia.com/nemo/relay/v0.10.0/llms.txt - v0.9.4: https://docs.nvidia.com/nemo/relay/v0.9.4/llms.txt - v0.8.4: https://docs.nvidia.com/nemo/relay/v0.8.4/llms.txt - v0.7.3: https://docs.nvidia.com/nemo/relay/v0.7.3/llms.txt - v0.6.0: https://docs.nvidia.com/nemo/relay/v0.6.0/llms.txt - v0.5.0: https://docs.nvidia.com/nemo/relay/v0.5.0/llms.txt

Use the Adaptive plugin when you want NeMo Relay to collect runtime signals and activate measured adaptive behavior through the shared plugin system.

Adaptive is a first-party plugin component with kind adaptive. It uses the same runtime model as the rest of NeMo Relay: scopes and managed calls emit lifecycle events, subscribers and learners observe those events, intercepts can add guidance, and plugin configuration controls what is active.

The plugin can coordinate:

• Adaptive state for learned runtime signals.
• Telemetry subscribers for adaptive learners.
• Adaptive hints injected into outgoing model requests.
• Tool-parallelism observation or scheduling behavior.
• Adaptive Cache Governor (ACG) prompt-cache planning.
• Opt-in response caching for repeated LLM calls and explicitly classified tool results.
• Component-local validation policy.

Use Adaptive When

Adaptive is useful when an agent workflow repeats similar work and you want to observe or tune behavior without hard-coding tuning logic into every application.

Start here when you need to:

• Collect runtime signals before changing behavior.
• Add model-request hints in a controlled way.
• Plan prompt-cache breakpoints for supported providers.
• Evaluate tool parallelism opportunities.
• Share adaptive state across workers when needed.
• Roll out measured tuning as a configuration change.

If instrumentation is not in place yet, start with Instrument Applications or Integrate into Frameworks.

Pages

• Adaptive Configuration documents the full plugin component shape, validation, activation, teardown, and whole-plugin settings.
• ACG explains Adaptive Cache Governor configuration and what prompt cache planning accomplishes.
• Adaptive Hints explains request hint injection and how downstream model paths can consume the hints.
• Response Cache explains the opt-in LLM response and tool-result cache: turning it on, what gets cached, and how savings are reported.

State, telemetry, tool parallelism, and policy are whole-plugin configuration areas. They are documented on Adaptive Configuration rather than as separate area pages.