MorphMorph
Morph Reflexes

Catch what your traces can't see

Most agent failures never throw an error. The agent loops, a user gives up, a jailbreak slips past, and the trace still says 200 OK. Reflexes label every turn.
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User frustration rate falling from 17% to 7.3% over 30 days, tracked turn by turn

AI agents fail long before they throw errors.

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import requests
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# Label every turn inline. One forward pass, zero decode.
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res = requests.post(
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    "https://api.morphllm.com/v1/reflex/predict",
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    headers={"Authorization": f"Bearer {MORPH_API_KEY}"},
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    json={"model": "user-frustration", "text": user_message},
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).json()
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# {
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#   "model": "user-frustration",
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#   "mode": "single_label",
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#   "classes": [
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#     {"label": "frustrated", "score": 0.97, "selected": true},
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#     {"label": "neutral",    "score": 0.03, "selected": false}
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#   ],
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#   "inference_time_ms": 41
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# }
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labels = [c["label"] for c in res["classes"] if c["selected"]]
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if "frustrated" in labels:
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    route_to_human(conversation)
Morph Reflexes

The feedback loop for self-improving agents

Detect what error messages miss

A request returns 200 while the conversation falls apart. Reflexes read every turn and flag the failures that never throw: frustration, looping, jailbreaks, leaked reasoning.

Two lines start together: the request line stays level while the experience line drifts away, flagged at a green node

Every turn, not a sample

LLM-as-judge is too slow and costly to run on everything, so teams sample, and sampling misses the turn that mattered. Reflexes run on all of it: ~15ms a check, from $0.00025 realtime, $0.00001 batch.

The same signal twice: sparse sample points straddle and miss a spike, a dense per-turn comb catches it in green

Bring your own signal

Describe the signal in a prompt and label a few edge cases. A custom reflex trains in under 30 minutes, no dataset required.

Wandering candidate boundaries converge through a few labeled points into one sharp green decision curve

Close the loop: self-improving agents

Every label is a training signal. Flagged conversations flow back into your evals, prompts, and fine-tuning runs, so the turn that frustrated a user yesterday becomes the test your agent passes tomorrow.

A conversation line winds into an inward spiral, each revolution tighter, converging on a glowing green center

Built to run on every turn

Track behavior over time, not one trace at a time

Track behavior over time, not one trace at a time

Request a Reflexes demo

Tell us what you're building. We'll tailor the demo to your stack.

Takes 15 sec

Turn the failures you can't see into an agent that improves.

Book demo

Reflexes is in private beta. The fine-tuning API is live in the docs and may change before general availability.