Adaptive Configuration#

Use this page when you want to configure the built-in Adaptive plugin component as a whole. The component kind is adaptive.

Adaptive plugin configuration uses the generic NeMo Flow plugin document shape. Field names stay snake_case in every binding and in plugins.toml, even when language helper functions use language-native naming conventions.

For plugin file discovery, precedence, merge behavior, editor controls, and gateway conflict rules, see Plugin Configuration Files.

Component Shape#

The top-level adaptive object contains:

Field	Purpose
`version`	Adaptive config schema version. Defaults to `1`.
`agent_id`	Stable logical agent or workflow identifier for learned state.
`state`	Adaptive state backend.
`telemetry`	Adaptive subscriber and learner settings.
`adaptive_hints`	Request hint-injection behavior.
`tool_parallelism`	Tool scheduling observation or scheduling behavior.
`acg`	Adaptive Cache Governor prompt-cache planning.
`policy`	Adaptive-local handling for unknown fields and unsupported values.

The requested area pages cover Adaptive Cache Governor (ACG) and Adaptive Hints. State, telemetry, tool parallelism, and policy remain whole-plugin settings:

Use state.backend.kind = "in_memory" for local experiments.
Use Redis state when learned state must survive restarts or be shared across workers.
Enable telemetry when adaptive learners should consume runtime events.
Keep tool_parallelism.mode = "observe_only" until scheduling behavior has been validated.
Keep policy.unsupported_value = "error" for rollout safety.

`plugins.toml` Example#

version = 1

[[components]]
kind = "adaptive"
enabled = true

[components.config]
version = 1
agent_id = "planner"

[components.config.state.backend]
kind = "in_memory"

[components.config.telemetry]
subscriber_name = "adaptive.telemetry"
learners = ["tool_parallelism"]

[components.config.tool_parallelism]
mode = "observe_only"
priority = 100

[components.config.adaptive_hints]
priority = 100
break_chain = false
inject_header = true
inject_body_path = "nvext.agent_hints"

[components.config.acg]
provider = "passthrough"
observation_window = 100
priority = 50

[components.config.acg.stability_thresholds]
stable_threshold = 0.95
semi_stable_threshold = 0.50
min_observations_for_full_confidence = 20

[components.config.policy]
unknown_component = "warn"
unknown_field = "warn"
unsupported_value = "error"

This configuration activates adaptive telemetry, keeps tool parallelism observational, injects adaptive hints, and leaves ACG in passthrough mode so requests can be observed without provider-specific cache translation.

Per-Language Plugin Configuration#

Python

import nemo_flow

adaptive_config = nemo_flow.adaptive.AdaptiveConfig(
    agent_id="planner",
    state=nemo_flow.adaptive.StateConfig(
        backend=nemo_flow.adaptive.BackendSpec.in_memory(),
    ),
    telemetry=nemo_flow.adaptive.TelemetryConfig(
        subscriber_name="adaptive.telemetry",
        learners=["tool_parallelism"],
    ),
    tool_parallelism=nemo_flow.adaptive.ToolParallelismConfig(mode="observe_only"),
    adaptive_hints=nemo_flow.adaptive.AdaptiveHintsConfig(
        inject_body_path="nvext.agent_hints",
    ),
    acg=nemo_flow.adaptive.AcgConfig(provider="passthrough"),
)

plugin_config = nemo_flow.plugin.PluginConfig(
    components=[nemo_flow.adaptive.ComponentSpec(adaptive_config)]
)

report = nemo_flow.plugin.validate(plugin_config)
if any(diagnostic["level"] == "error" for diagnostic in report["diagnostics"]):
    raise RuntimeError(report["diagnostics"])

active = await nemo_flow.plugin.initialize(plugin_config)

Node.js

const adaptive = require("nemo-flow-node/adaptive");
const plugin = require("nemo-flow-node/plugin");

const adaptiveConfig = adaptive.defaultConfig();
adaptiveConfig.agent_id = "planner";
adaptiveConfig.state = { backend: adaptive.inMemoryBackend() };
adaptiveConfig.telemetry = adaptive.telemetryConfig({
  subscriber_name: "adaptive.telemetry",
  learners: ["tool_parallelism"],
});
adaptiveConfig.tool_parallelism = adaptive.toolParallelismConfig({ mode: "observe_only" });
adaptiveConfig.adaptive_hints = adaptive.adaptiveHintsConfig({
  inject_body_path: "nvext.agent_hints",
});
adaptiveConfig.acg = adaptive.acgConfig({ provider: "passthrough" });

const pluginConfig = plugin.defaultConfig();
pluginConfig.components = [adaptive.ComponentSpec(adaptiveConfig)];

const report = plugin.validate(pluginConfig);
if (report.diagnostics.some((diagnostic) => diagnostic.level === "error")) {
  throw new Error(JSON.stringify(report.diagnostics));
}

const active = await plugin.initialize(pluginConfig);

Rust

use nemo_flow::plugin::{initialize_plugins, validate_plugin_config, PluginConfig};
use nemo_flow_adaptive::plugin_component::ComponentSpec;
use nemo_flow_adaptive::{
    AdaptiveConfig,
    BackendSpec,
    StateConfig,
    TelemetryComponentConfig,
    ToolParallelismComponentConfig,
    AdaptiveHintsComponentConfig,
    AcgComponentConfig,
};

let mut adaptive = AdaptiveConfig::default();
adaptive.agent_id = Some("planner".into());
adaptive.state = Some(StateConfig {
    backend: BackendSpec::in_memory(),
});
adaptive.telemetry = Some(TelemetryComponentConfig {
    subscriber_name: Some("adaptive.telemetry".into()),
    learners: vec!["tool_parallelism".into()],
});
adaptive.tool_parallelism = Some(ToolParallelismComponentConfig::default());
adaptive.adaptive_hints = Some(AdaptiveHintsComponentConfig {
    inject_body_path: "nvext.agent_hints".into(),
    ..AdaptiveHintsComponentConfig::default()
});
adaptive.acg = Some(AcgComponentConfig {
    provider: "passthrough".into(),
    ..AcgComponentConfig::default()
});

let mut plugin_config = PluginConfig::default();
plugin_config.components.push(ComponentSpec::new(adaptive).into());

let report = validate_plugin_config(&plugin_config);
assert!(!report.has_errors());

let active = initialize_plugins(plugin_config).await?;

Manual API#

Use the manual runtime API when an integration needs to own adaptive lifecycle directly instead of activating the top-level plugin component.

Python

import nemo_flow

adaptive_config = nemo_flow.adaptive.AdaptiveConfig(
    agent_id="planner",
    state=nemo_flow.adaptive.StateConfig(
        backend=nemo_flow.adaptive.BackendSpec.in_memory(),
    ),
    telemetry=nemo_flow.adaptive.TelemetryConfig(
        subscriber_name="adaptive.telemetry",
        learners=["tool_parallelism"],
    ),
    tool_parallelism=nemo_flow.adaptive.ToolParallelismConfig(mode="observe_only"),
    adaptive_hints=nemo_flow.adaptive.AdaptiveHintsConfig(
        inject_body_path="nvext.agent_hints",
    ),
    acg=nemo_flow.adaptive.AcgConfig(provider="passthrough"),
)

runtime = nemo_flow.adaptive.AdaptiveRuntime(adaptive_config.to_dict())
await runtime.register()
try:
    # Run instrumented application work here.
    runtime.wait_for_idle()
finally:
    await runtime.shutdown()

Node.js

The Node.js binding exposes the built-in adaptive runtime through the adaptive plugin component helpers. Use the Plugin Configuration example above when activating adaptive behavior from Node.js.

Rust

use nemo_flow_adaptive::{
    AcgComponentConfig, AdaptiveConfig, AdaptiveHintsComponentConfig, AdaptiveRuntime,
    BackendSpec, StateConfig, TelemetryComponentConfig, ToolParallelismComponentConfig,
};

let mut adaptive = AdaptiveConfig::default();
adaptive.agent_id = Some("planner".into());
adaptive.state = Some(StateConfig {
    backend: BackendSpec::in_memory(),
});
adaptive.telemetry = Some(TelemetryComponentConfig {
    subscriber_name: Some("adaptive.telemetry".into()),
    learners: vec!["tool_parallelism".into()],
});
adaptive.tool_parallelism = Some(ToolParallelismComponentConfig::default());
adaptive.adaptive_hints = Some(AdaptiveHintsComponentConfig {
    inject_body_path: "nvext.agent_hints".into(),
    ..AdaptiveHintsComponentConfig::default()
});
adaptive.acg = Some(AcgComponentConfig {
    provider: "passthrough".into(),
    ..AcgComponentConfig::default()
});

let mut runtime = AdaptiveRuntime::new(adaptive).await?;
runtime.register().await?;

// Run instrumented application work here.

runtime.wait_for_idle();
runtime.shutdown().await?;

Validation And Teardown#

Validate plugin configuration before initialization. Disabled adaptive components are still validated, which lets operators prepare a rollout before setting enabled = true.

Common validation failures include:

Unknown adaptive fields when policy treats unknown fields as errors.
Unsupported backend kinds, tool-parallelism modes, or ACG providers.
Unsupported schema versions.
Backend-specific fields that do not match the selected backend.

Clear plugin configuration during shutdown or test cleanup. Clearing the plugin configuration deregisters adaptive subscribers and intercepts owned by the plugin runtime.

Rollout Guidance#

Start by enabling state and telemetry in a development environment. Run representative instrumented workflows, inspect emitted events and adaptive reports, and then enable active behavior one area at a time. Keep rollback as a configuration change.