DSPy program evaluation telemetry

Test the integration

Send and verify events with DSPy program evaluation telemetry

Use DSPy program evaluation telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.

1
Choose the outcome
DSPy program reliability
2
Define the contract
operation_id, program_name, program_version, model_alias, optimizer_name, and release
3
Log the final result
Wrap the public DSPy module call for production outcomes and emit evaluator results separately after dspy.Evaluate completes.
4
Check the stored event
Exercise a known fixture, then inspect dspy_program_completed for one correctly typed terminal row.

Before you start

DSPy and telemetry-sh initialized in a trusted Python process
Stable program, model, optimizer, dataset, metric, and release versions
Separate event grains for live predictions and offline evaluation results

Delivery setup

Install and initialize server-side

Initialize Telemetry for synchronous code or TelemetryAsync for asyncio code once per process with a server-side key. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

dspy-install

pip installation

bash

python -m pip install telemetry-sh

1Create one reusable server-side client. Set its timeout and retry limit.
2Log an event when the operation succeeds, fails, retries, or times out.
3Send test events with known results and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

dspy

DSPy program evaluation telemetry event

python

from time import perf_counter

def run_support_program(program, question: str, operation_id: str):
    started_at = perf_counter()
    status = "success"
    error_type = None

    try:
        return program(question=question)
    except Exception as error:
        status = "failed"
        error_type = classify_program_error(error)
        raise
    finally:
        telemetry.log("dspy_program_completed", {
            "operation_id": operation_id,
            "program_name": "support_answer",
            "program_version": PROGRAM_VERSION,
            "model_alias": MODEL_ALIAS,
            "status": status,
            "error_type": error_type,
            "duration_ms": round((perf_counter() - started_at) * 1000),
            "release": APP_RELEASE,
        })

Event schema

operation_id, program_name, program_version, model_alias, optimizer_name, and release

status, duration_ms, estimated_cost_usd, accepted, and error_type

dataset_version, metric_name, metric_version, evaluation_score, passed, and evaluated_at for evaluation events

Check your setup

Checkpoint 1

Wrap the public DSPy module call for production outcomes and emit evaluator results separately after dspy.Evaluate completes.

Checkpoint 2

Do not compare optimizer candidates that used different datasets, metrics, judge models, thresholds, or evaluation coverage without separating those versions.

Checkpoint 3

Keep examples, predictions, traces, metric feedback, prompts, and model history in the approved DSPy or observability workflow rather than copying them into Telemetry.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

dspy-verification

DSPy program evaluation telemetry verification query

sql

SELECT *
FROM dspy_program_completed
ORDER BY timestamp_utc DESC
LIMIT 20;

Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.

Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.

Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.

Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

Evaluate AI agents with SQL AI quality regression SQL Accepted output per dollar SQL DSPy metrics and evaluation DSPy debugging and observability DSPy tools with ReAct

Where to log

Keep the outcome event small and recoverable

This pattern provides

Record the outcome as an event you can query with SQL.
Stable fields for dashboards, alerts, and cross-event correlation.
Test events for checking success, failure, retries, and timeouts.

This pattern does not provide

An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
Exactly-once delivery merely because the payload contains an event ID.
Permission to collect raw provider payloads, user content, credentials, or regulated data.

Delivery and idempotency Batching and shutdown Troubleshoot ingestion

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

AI operations10 fields