How to Add Per-Node Trace Policies in LangGraph 1.2.11

LangGraph 1.2.11 shipped on August 11 with a new trace_policy parameter on add_node, giving you fine-grained control over what gets recorded in traces (LangSmith, OpenTelemetry, etc.) at the level of an individual graph node — rather than tracing everything a node sees and produces, or nothing at all.

This matters most for production agent graphs where individual nodes handle large state objects, sensitive data, or high-frequency calls where full-payload tracing adds unnecessary latency or noise.

What trace_policy Actually Does

Per the merged pull request that introduced the feature (langchain-ai/langgraph #8523), trace_policy accepts a TracePolicy object with two optional callables:

from dataclasses import dataclass
from typing import Any, Callable

@dataclass
class TracePolicy:
    process_inputs: Callable[[Any], Any] | None = None
    process_outputs: Callable[[Any], Any] | None = None

• process_inputs — a callable applied to the node’s input before it’s recorded in the trace. It receives the real, untransformed input and should return whatever value you want to show up in the trace instead.
• process_outputs — same idea, applied to the node’s output before it’s recorded.

Critically, per the actual implementation, these transformations only affect what gets recorded, never what the node actually receives or returns during execution. If your process_inputs or process_outputs callable raises an exception, the underlying code catches it, logs the exception, and falls back to recording the untransformed value rather than breaking the run:

def _trace_payload(value: Any, transform: Callable[[Any], Any] | None) -> Any:
    if transform is None:
        return value
    try:
        return transform(value)
    except Exception:
        logger.exception(
            "trace input/output processor raised; recording untransformed payload"
        )
        return value

That fail-safe behavior is a meaningful design detail: a bug in your trace-scrubbing logic can’t take down your actual agent execution — it can only cause you to see more (untransformed) data in your trace than you intended.

Using trace_policy on add_node

Based on the merged implementation, trace_policy is a keyword argument on add_node (and the related node-adding methods internally):

from langgraph.graph import StateGraph
from langgraph.types import TracePolicy

builder = StateGraph(State)

def my_node(state):
    ...

builder.add_node(
    "my_node",
    my_node,
    trace_policy=TracePolicy(
        process_inputs=lambda inp: {"scrubbed": True},
        process_outputs=lambda out: {"scrubbed": True},
    ),
)

Using the Built-In omit_payload Helper

For the common case — you don’t want to write a custom scrubbing function, you just want a node’s input or output dropped entirely from the trace — LangGraph ships a helper alongside TracePolicy:

from langgraph.types import TracePolicy, omit_payload

builder.add_node(
    "sensitive_node",
    sensitive_node,
    trace_policy=TracePolicy(
        process_inputs=omit_payload,
        process_outputs=omit_payload,
    ),
)

omit_payload records an empty payload, dropping the value entirely from what’s traced — useful for nodes that handle large state blobs (bulk documents, embeddings, file contents) where you want visibility that the node ran, but not the full payload cluttering your trace viewer or exceeding a tracing backend’s size limits.

Applying It Only Where You Need It

Because trace_policy is scoped per-node rather than global, you only need to specify it on the nodes where it matters — unspecified nodes continue recording exactly as before (a full passthrough), so this is purely additive and doesn’t require touching your existing graph definition elsewhere.

A practical pattern for a graph with a mix of routine and sensitive nodes:

builder = StateGraph(State)

# Routine node — no trace_policy needed, records normally
builder.add_node("classify_intent", classify_intent)

# Node handling large document content — drop it from traces
builder.add_node(
    "retrieve_documents",
    retrieve_documents,
    trace_policy=TracePolicy(process_outputs=omit_payload),
)

# Node handling PII — scrub before recording, don't drop entirely
def redact_pii(output: dict) -> dict:
    redacted = dict(output)
    redacted.pop("customer_email", None)
    redacted.pop("customer_phone", None)
    return redacted

builder.add_node(
    "lookup_customer",
    lookup_customer,
    trace_policy=TracePolicy(process_outputs=redact_pii),
)

Why This Ships Now

trace_policy builds on LangGraph’s existing LangSmith/OpenTelemetry tracing integrations rather than introducing a new tracing backend. The motivation, per the PR, is specifically middleware and long-running nodes where full-state tracing adds meaningful latency or clutter — a natural pain point as more teams run production multi-agent graphs with larger, more complex state objects flowing between nodes.

The release also includes a checkpoint delta-channel fix (collecting writes at plain-value seed correctly) and routine dependency bumps — solid maintenance work, but trace_policy is the substantive new capability in 1.2.11.

A Note on Version Requirements

trace_policy requires LangGraph 1.2.11 or later — check your installed version before assuming the parameter is available:

pip show langgraph | grep Version

If you’re on an earlier 1.2.x release, upgrade with:

pip install --upgrade langgraph

Sources

1. LangGraph GitHub Release — langgraph==1.2.11
2. langchain-ai/langgraph PR #8523: feat(langgraph): expose trace_policy on add_node

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