The Tool Desk
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Understand what a LangGraph trace can contain
LangSmith records work as runs—such as model calls, tool invocations, and retrieval operations—and groups related runs into a trace. LangChain’s observability documentation says runs include their full inputs and outputs and that one trace can contain up to 25,000 runs. A sensitive value can therefore appear beyond the top-level user prompt: inspect nested tool arguments, retrieved content, model responses, errors, and custom state representations.
Before choosing a control, separate the data paths you need to govern:
- Trace export: inputs, outputs, and metadata sent by the tracing client to LangSmith.
- Model input: the prompt and other data passed to the model.
- Graph persistence: checkpointed execution state and other server resources.
- Other retention: application logs and any data retained by model providers or other integrations.
LangSmith masking operates on the trace-export path. It should not be treated as prompt redaction or as a control over every other storage location.
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Choose the right trace-protection control
LangSmith documents controls for hiding complete inputs or outputs, transforming selected fields, filtering metadata, and suppressing tracing conditionally. The right choice depends on whether you need debugging detail, reduced exposure, or no trace for a particular request.
| Control | What it does | When it fits | Important limit |
|---|---|---|---|
| Hide inputs or outputs | Removes the selected payload from traces; callbacks can also return an empty object or a transformed safe subset. | Use when broad data minimization matters more than inspecting the original payload. | Inputs and outputs are separate controls. Configure each deliberately. |
| Filter or transform metadata | Removes metadata with hide_metadata=True or applies a callable to select or transform it. |
Use when tags, identifiers, or custom fields may contain sensitive values. | Metadata needs its own policy; hiding inputs and outputs does not automatically establish one. |
| SDK anonymizer | Applies regex patterns and replacement values or a custom function; documented examples include email and UUID substitutions. | Use when you want to preserve a transformed trace for debugging. | Patterns and example implementations are not complete guarantees. Validate against your data. |
| Third-party anonymization | The guide documents integrations using Microsoft Presidio and Amazon Comprehend. | Consider when a detector-based approach better fits the data and operational model. | External detection changes the data flow and can add latency, cost, and service limits. |
| Conditional tracing | Prevents a trace from being sent for selected requests. | Use when policy requires that a request not be traced at all. | It suppresses LangSmith tracing for that request; it does not disable other logging or persistence systems. |
LangChain’s guide to preventing sensitive data from being logged in traces documents environment variables LANGSMITH_HIDE_INPUTS=true and LANGSMITH_HIDE_OUTPUTS=true, plus per-client callbacks: hide_inputs and hide_outputs in Python, and hideInputs and hideOutputs in TypeScript. A function decorated with @traceable can use process_inputs and process_outputs to log a transformed representation for that function. When function-level processors and client-level input/output processors are both defined, the function-level processors take precedence.
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Apply separate policies to inputs, outputs, and metadata
Inventory where sensitive data can occur before configuring callbacks. A user message may contain personal information, but so may a tool result, retrieved document, model response, exception, or custom field. Decide which fields must disappear, which can be safely transformed, and which are acceptable to retain. Treat metadata as a separate data class rather than assuming payload controls cover it.
LangSmith supports hide-all behavior as well as callbacks that return only a safe subset. This lets a team retain useful, deliberately selected debugging context without exporting the full payload. Use the narrowest safe representation that still answers the operational question your traces need to answer.
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For selective anonymization, the LangSmith SDK supports regex-and-replacement rules or a custom function. The documentation also describes Microsoft Presidio and Amazon Comprehend examples. A detector service is not automatically safer: assess what text is sent to that service, its operational limits, and the resulting latency and throughput impact in your own environment.
Know the limits of masking and anonymization
The official masking guide warns that its regex example is not exhaustive and may miss formats or edge cases. A pattern that matches a conventional email or credit-card number may fail on localized formats, obfuscated text, unusual encodings, secrets nested in objects, or a custom state structure.
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- The SDK anonymizer traverses to a default maximum depth of 10 nesting levels. Check data deeper than that and test non-string values deliberately.
- The anonymizer serializes payloads to JSON before processing. Large payloads or complex regular expressions can add overhead; the documentation does not provide a performance benchmark for your application.
- The documented Amazon Comprehend example makes one PII-detection API call per string, so it may become rate-limit-bound. The example uses a 100 KB per-call text limit and leaves oversized text unchanged; it is explicitly not exhaustive.
- Regex and detector-based approaches should not be described as guaranteeing complete PII removal. Measure their behavior against representative data and account for cases they do not detect.
The same guide lists rule-based masking as available in LangSmith SDK Python 0.1.81 and above and TypeScript 0.1.33 and above; function-level process_outputs is listed for Python SDK 0.1.98 and above. These are documentation-stated version floors, not independent compatibility tests. Check the current guide when selecting or upgrading an SDK.
Disable tracing when a request must not be recorded
Masking changes what a trace contains; it still permits a trace to be sent. For a strict no-trace requirement, use conditional tracing, as LangSmith’s guide recommends, rather than treating redaction as equivalent to non-collection.
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Enforce the decision where the request is handled, and verify that the condition cannot be bypassed by a client configuration or a tracing decorator that was omitted or configured differently. A LangSmith tracing decision is only one part of a retention policy: review application logs, checkpoints, server resources, and model-provider paths separately.
Account for Agent Server destinations and persisted state
For LangGraph Agent Server, the available trace destination depends on deployment mode. According to LangChain’s deployment architecture documentation, Cloud requires tracing to LangSmith SaaS. Hybrid and Self-Hosted deployments offer optional tracing and additional destination choices, including Self-Hosted LangSmith for Self-Hosted deployments.
Tracing and graph-state persistence are distinct. LangChain’s data-plane documentation says PostgreSQL stores server resources, including runs, and is the default checkpoint backend for graph execution state. MongoDB can be used as an alternative for checkpoint storage, while PostgreSQL remains required for other server resources. Review checkpoint and data-plane retention separately from trace masking; changing what is exported in a trace does not establish what is retained in persisted graph state.
Redact model inputs upstream when needed
If the goal is to prevent the model from receiving certain sensitive values, use an upstream input-protection control in addition to trace controls. Arcjet’s documented LangChain JavaScript integration wraps an LLM, redacts selected entities from prompt text before it is sent to that LLM, then unredacts the response before returning it. Its examples list email, phone number, IP address, and credit card entities, along with custom detection and replacement hooks.
This is a different point in the flow from LangSmith masking: upstream redaction changes what reaches the model, while LangSmith controls what the tracing client sends to the tracing backend. Confirm the integration’s current behavior and fit for your language and threat model; it does not replace trace controls.
Quick Recap
Roll out and verify the policy
- Map data paths. Identify trace-producing runs, sensitive inputs and outputs, metadata, tool and retrieval payloads, errors, model calls, checkpoint storage, and application logs.
- Set retention requirements by data class. Decide what must be hidden, what can be transformed into a safe subset, what metadata can remain, and which requests must not be traced.
- Configure controls at the appropriate layer. Set input, output, and metadata policies on tracing clients; use function-level processors where suitable; use conditional tracing for requests that must not be collected.
- Test with representative synthetic or approved data. Assert that known sensitive markers are absent from every emitted trace field, including nested tool inputs, outputs, metadata, and error paths. Test relevant nesting depths and formats.
- Test failure and omission paths. Check behavior when a client or decorator lacks the protection, and verify that high-risk requests are suppressed at the point they are handled.
- Measure operational impact. Assess latency and throughput in your own environment, especially before adopting complex regex processing or a per-string external detection service.
- Review other retention systems. Confirm separately how checkpoints, server resources, application logs, and provider integrations retain data.
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