Your AI nails the demo. TruLayer makes it nail production.
Evals score every output. A closed control loop retries with a fallback model, gates high-stakes actions on human approval, and rolls back automatically when a fix introduces a new regression — turning every production failure into a system fix before it hits the next user.
Free tier includes 1M spans / month · No credit card
Works with
How teams use TruLayer
Observe. Evaluate. Improve.
Most tools stop at the trace. TruLayer takes you from “something’s wrong” to “here’s the fix, shipped” in one platform.
Observe
See what’s happening. Understand why.
Distributed traces, failure clustering, anomaly detection, and semantic search — everything you need to go from "something’s wrong" to "here’s exactly why." Configurable retry depth prevents runaway cascades: when a trace has been retried N times without passing eval, it escalates to the human-in-the-loop queue automatically.
Explore observeEvaluate
Know whether it was correct, not just whether it ran.
25 pre-built evaluators, eval rules on any span, regression testing against golden datasets, and score trends over time.
Explore evaluateImprove
Close the loop before the next user hits it.
AI-suggested prompt improvements, self-healing actions, human-in-the-loop approval, and remediation diffs — the full control loop.
Explore improveWho uses TruLayer
AI agents handle millions of decisions. Here’s where they go wrong.
TruLayer keeps them on track — automatically, at the system level, before the same failure hits the next user.
Customer & Revenue
Customer Support Agents
Thousands of refund decisions a day. One bad policy interpretation costs you twice.
Your refund agent handles thousands of decisions a day. One bad policy interpretation issues $1,000 instead of $500, another invents a department that doesn’t exist — and you find out from a support ticket, not a trace. Eval rules score every refund decision inline. When a rule fires, the control loop retries with a corrected prompt or routes the case to a human queue — the next customer gets the right answer.
Explore customer support agentsOutbound Sales Agents
Deprecated pricing, opted-out prospects, and a deal that collapsed.
Your SDR agent quoted a pricing tier you deprecated six months ago, then emailed a prospect who had opted out last quarter. The deal collapsed; legal is now involved. Faithfulness scoring flags outputs that drift from your pricing and compliance context. The same failure class doesn’t reach the send queue twice.
Explore outbound sales agentsEngineering & Operations
Agentic Coding Agents
Wrong-scope refactor. Deleted file. Test edited to pass. Found at CI, hours late.
Your coding agent refactored a module that a parallel branch already rewrote, deleted a file based on a truncated context window, and edited a test to make it pass. CI catches the deletion hours after the agent session closed; the rest only surfaces in staging. Function-call correctness, prompt injection, and faithfulness evaluators score every tool call inline. When a rule fires, the control loop retries with a corrected file scope or routes the next agent run on the same failure path to a human review queue.
Explore agentic coding agentsAI Ops Agents
Restarted the wrong service. Now you have two incidents.
Your incident response agent restarted a healthy service that shared a label with the degraded one. Now you have two incidents and it’s 2am. Tool-call correctness evaluators score every automated action inline. When a rule fires, the loop routes to a human before the next runbook step executes — not after the postmortem.
Explore ai ops agentsData & Documents
Document Extraction Agents
Wrong total line. Dropped tax ID. Silent type mismatch. Pipeline reported success.
Your invoice extraction agent read the subtotal line instead of total-due and wrote the wrong amount to your ERP. A second invoice dropped a vendor tax ID because the field label varied from the template — the PII landed in the CRM anyway. A third had its amount field silently coerced to string; the type validation failed, the pipeline reported success, and nobody found out until reconciliation. PII leakage and tool-call correctness evaluators run inline on every span. When an extracted field is missing, malformed, or the wrong type, the control loop retries with a targeted prompt, falls back to a stricter extraction template, or routes to a human review queue — before the record propagates downstream.
Explore document extraction agentsFinance & Reporting Agents
Last quarter’s forecast. This quarter’s actuals. One board deck.
Your analyst agent applied last quarter’s forecast model to this quarter’s actuals. The board deck had the wrong numbers. The model produced a clean, confident summary with no error indicators. Faithfulness scoring catches context-window drift before it propagates. The control loop retries with corrected grounding context; the trace shows exactly where the eval score dropped.
Explore finance & reporting agentsRegulated & High-Stakes
Legal Research Agents
The citation looked right. The case doesn’t exist.
Your research agent cited a case that doesn’t exist. The associate submitted the brief. Opposing counsel found it in writing. Hallucination and faithfulness evaluators run on every span. When a citation diverges from verified sources, the control loop escalates to a human reviewer instead of letting it move downstream.
Explore legal research agentsClinical AI Assistants
Wrong dosage range. Delivered with full confidence.
Your health assistant described the right condition with the wrong dosage range — confidently, with no indication it was wrong. Nothing in the pipeline flagged it. Clinical faithfulness scoring runs inline as each trace arrives, not in a nightly batch. Outputs that deviate from grounding context route to a clinical review queue automatically.
Explore clinical ai assistantsInstrument in minutes
Two lines. Full visibility.
Wrap any OpenAI, Anthropic, or custom LLM client with TruLayer. Every call, chain, and tool use is automatically traced — no manual spans, no config files.
- ✓Auto-captures inputs, outputs, tokens, latency, cost
- ✓Propagates trace context across async agent hops
- ✓Zero overhead — proxy-based, not monkey-patching
import { TruLayer } from '@trulayer/sdk'
import OpenAI from 'openai'
const tl = new TruLayer({ apiKey: process.env.TRULAYER_API_KEY })
const openai = tl.instrument(new OpenAI())
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [{ role: 'user', content: task }],
tools: agentTools,
})// TruLayer automatically captures:
// ✓ inputs, outputs, tool calls
// ✓ token usage and cost per hop
// ✓ latency and eval scores on every span
// ✓ trace context across async agent stepsHow it works
From deploy to confidence in three steps
Instrument
Wrap your LLM client with two lines of code. Supports OpenAI, Anthropic, and any custom model.
Observe
Every agent step, tool call, and chain hop appears in your trace explorer in real time.
Evaluate & Fix
Automatic evals score every output. Failure alerts and auto-remediation close the loop.
Built for this
Purpose-built for production AI
TruLayer is purpose-built for teams shipping LLM features to production — not adapted from general-purpose APM.
One stack, not three stitched together
You already have traces, evals, and a feedback queue — in three different tools, with three integrations to maintain and no shared data model. TruLayer puts tracing, automated evaluation, and human feedback in a single pipeline. One place to see what ran, how it scored, and what to fix.
OTEL-native from day one
If your stack already speaks OpenTelemetry, TruLayer fits without a rewrite. Send spans over OTLP — same exporter you use for everything else. No proprietary SDK to lock in, no parallel instrumentation to maintain. When you outgrow TruLayer, your traces leave with you.
First trace in under five minutes
Sign up, add two lines, see your first trace. No YAML, no collector config, no support ticket. The quickstart is written for engineers who have a running agent today — not for teams evaluating a six-month rollout.
Ready to see your first trace? Start the quickstart
Pricing
Simple, usage-aligned pricing
Start free. Scale with your usage. No credit card required on Starter.
Starter
No credit card required
- 1M spans / month
- 3 seats
- 2.5K evals / month
- Anomaly detection
- 30-day retention
- Docs & GitHub support
Pro
$5 / additional 1M spans
- 20M spans / month
- 5 seats
- 90-day retention
- 50K evals / month
- Anomaly detection + webhooks
- Semantic search
- Email support
Team
$4 / additional 1M spans
- 100M spans / month
- 15 seats
- 180-day retention
- 250K evals / month
- SSO / SAML included
- Slack support (trulayerai.slack.com)
Biz
Coming soonLarge teams beyond 15 seats. Contact us for custom pricing.
Enterprise
Custom volume · Dedicated support · Compliance-ready
Need custom volume, procurement, or dedicated support? Let's talk.
Annual billing saves 20% on the Team plan. No credit card required on Starter.
Reliable AI.
Not just observable AI.
Observability tells you what broke. TruLayer tells you what broke, why, and fixes it automatically. Start free.
Start free