What Valiqor Does

Valiqor finds why your AI app fails

Most evaluation tools tell you something is wrong. Valiqor tells you what went wrong, why it happened, and how to fix it — with severity scoring, root-cause analysis, and actionable remediation.

You can get started in 5 minutes — no tracing or instrumentation needed. Just pass your existing AI inputs and outputs to Failure Analysis and see results immediately.

Five modules, one SDK

Valiqor’s Python SDK exposes five modules through a single client:

from valiqor import ValiqorClient

client = ValiqorClient(api_key="vq_...")

client.failure_analysis   # Root-cause failure detection
client.eval               # Metric-based evaluation
client.security           # Red-team audits & vulnerability scanning
client.trace              # Auto-instrumentation for OpenAI, Anthropic, LangChain
client.scanner            # Repository & prompt scanning

Module	What it does	When to use it
Failure Analysis	Classifies failures into buckets & subcategories, scores severity (0–5), explains root cause	First thing to run — works on any input/output data
Evaluation	Runs metric-based checks (hallucination, relevance, coherence, etc.)	When you need granular metric scores
Security	Runs red-team attacks, checks for prompt injection, data leakage, jailbreaks	Before deploying to production
Tracing	Auto-captures LLM calls, tool use, retrieval steps as structured traces	When you want continuous monitoring in production
Scanner	Scans repositories and prompts for best-practice violations	During code review or CI

Two ways to get started

Quick start — Run on existing data

Pass your AI inputs/outputs directly. No tracing, no instrumentation. See failure results in under 5 minutes.

Full observability — Add tracing

Instrument your LLM calls with auto-tracing. Run Failure Analysis on traces for continuous production monitoring.

Who is Valiqor for?

Valiqor is built for AI engineers and teams who build applications on top of:

LLM providers — OpenAI, Anthropic, Google, Mistral, Cohere, and any OpenAI-compatible API
Orchestration frameworks — LangChain, CrewAI, LlamaIndex, Haystack
Custom pipelines — RAG systems, agent loops, multi-step chains

Whether you’re prototyping or running in production, Valiqor helps you catch and fix failures before your users do.

What you get

Failure taxonomy

Every failure is classified into buckets (e.g., Hallucination, Context Ignorance) and subcategories (e.g., Entity Fabrication, Contradicts Source).

Root-cause analysis

Each failure includes a judge_rationale explaining exactly what went wrong, plus a scoring_breakdown with evidence.

Severity & confidence scoring

Severity from 0 (no issue) to 5 (critical). Confidence from 0.0 to 1.0. Filter and prioritize failures automatically.

Actionable remediation

Fix your prompts, retrieval pipeline, or guardrails based on concrete failure evidence — not guesswork.

Ready? See your first failure →

Run Failure Analysis on your existing data in under 5 minutes.

Start Here

Core Workflows

Concepts

Integrations

Resources

What Valiqor Does

Valiqor finds why your AI app fails

Five modules, one SDK

Two ways to get started

Quick start — Run on existing data

Full observability — Add tracing

Who is Valiqor for?

What you get

Ready? See your first failure →

​Valiqor finds why your AI app fails

​Five modules, one SDK

​Two ways to get started

Quick start — Run on existing data

Full observability — Add tracing

​Who is Valiqor for?

​What you get

Ready? See your first failure →

Valiqor finds why your AI app fails

Five modules, one SDK

Two ways to get started

Who is Valiqor for?

What you get