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The observability and evaluation layer for LLM apps and agents.
Trace every call. Score every output. Catch the regression.

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THE SIGNAL BEHIND THE RESPONSETRACE VIEW / 001

support_agent EXAMPLE RUN

Completed2.41s duration1,824 tokens$0.0038 cost

SPAN NAME

0s1.2s2.4s

TIME

EVALUATION SCORESfaithfulness 0.91relevance 0.871 failed check

5 spans capturedPython SDK / prompt v14

ENGINEERING TARGETSp95 overhead <5msOpenTelemetry native2 lines to integrate

TRACING

The answer is only
half the story.

Follow the request through every model call, retrieval, and tool. See the prompt, output, latency, and cost in context.

Nested spans. One complete picture.
Inputs and outputs, side by side.
The slow call is no longer a guess.

Follow a trace

TRACE EXPLORERDEMO DATA

Hover or select a span

PROMPT

Use the retrieved policy to answer the customer. Prompt v14.

OUTPUT

Yes, you can return your order. I’ll start a refund for you.

ESTIMATED COST$0.0038

EVALUATIONS

A better prompt?
Make it prove it.

Run the same cases against two versions. See what improved, what broke, and exactly which output needs another look.

Compare the evidence

support-agent-evalsDEMO DATA

prompt v13 prompt v14

TEST CASE	v13 BASELINE	v14 CANDIDATE	DELTA	RESULT
Return within window	0.92	0.97	+0.05	PASS
Return after 30 days	0.94	0.41	−0.53	FAIL
Missing order ID	0.86	0.93	+0.07	PASS
Partial refund request	0.88	0.91	+0.03	PASS
Policy citation	0.91	0.96	+0.05	PASS

Same dataset. Same rubric. Comparable results.4 passed / 1 failed

VERSIONED PROMPTSREPEATABLE DATASETSEXPLICIT RUBRICS

FAITHFULNESS OVER TIMEDEMO DATA

Faithfulness 0.72−0.22

1.00.80.6

09:0010:0011:0012:00

Regression detectedroot cause: prompt v14

11:04:32

REGRESSION ALERTS

Find the dip.
Then find the why.

A falling score is the starting point. Follow it back to the prompt version and the failing trace, while the evidence is still fresh.

From a signal to a specific call.

Inspect a failing runAlert timeline shown as a product preview.

FROM REQUEST TO RESOLUTION

A tighter feedback loop.

Follow the call. Test the change. Know what shipped.

Instrument

Connect the SDK and wrap your agent workflow. Capture the calls that make up a request.

client.trace("support-agent")

Evaluate

Run a dataset against your prompts. Score outputs with an explicit, versioned rubric.

faithfulness > 0.85

Iterate

Inspect failing cases, compare versions, and rerun the same tests before your next release.

v13 → v14 · compare

LESS GUESSWORK. BETTER AGENTS.

Your next deploy.
With the evidence.

See what your agent did. Know what to change.

Get startedExplore the demo. No API key needed.

See the trace.Ship with confidence.