Patronus AI
Patronus AI is an evaluation and guardrails platform for production LLM applications and AI agents. It combines an API-first evaluation service with Python and TypeScript SDKs, in-house judge models (Lynx for hallucination detection, Glider for reasoning evaluation, Percival for agent debugging), and a portfolio of open benchmarks and datasets including FinanceBench, BLUR, and RL environments. Customers use Patronus for experimentation, production monitoring, RAG and agent evaluation, dataset generation, and human-in-the-loop annotation.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Patronus AI the way a machine reads it — 30 machine-readable artifacts across 7 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Patronus AI scores 27.5/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Patronus AI
Each block below is one kind of artifact we hold for Patronus AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 7
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Patronus Evaluation API
The Patronus Evaluation API scores LLM outputs against built-in and custom evaluators covering hallucination, answer relevance, context utilization, safety, and PII. Evaluators ...
Patronus Python SDK
The Patronus Python SDK provides decorators and clients for instrumenting LLM applications, running evaluators inline, recording traces, and pushing experiments to the Patronus ...
Patronus TypeScript SDK
The Patronus TypeScript SDK brings the same evaluation, tracing, and experiment workflows to Node.js and browser environments used by JavaScript-first AI applications.
Lynx
Lynx is Patronus's open-weights hallucination detection model published on Hugging Face. It is positioned as state-of-the-art on hallucination benchmarks and is available both a...
Glider
Glider is Patronus's small judge model for evaluating reasoning chains and rubric-based scoring with low latency and cost relative to large frontier judges.
Percival
Percival is Patronus's agent debugging product that ingests agent traces and surfaces failure modes, tool misuse, and reasoning errors across multi-step runs.
FinanceBench
FinanceBench is an open benchmark of 10,000 financial question-answer pairs grounded in public filings, used to evaluate LLM performance on financial document understanding.
Scroll within the panel for all 7 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Patronus Ai Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Patronus Ai Finops
FINOPSFeatures 8
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Evaluation API
Hosted API for running built-in and custom evaluators on LLM inputs and outputs.
Lynx Hallucination Detection
State-of-the-art open-weights hallucination judge available as a hosted evaluator.
Glider Judge
Small reasoning-focused judge for rubric-based evaluation at production latency.
Percival Agent Debugger
Agent trace analysis surfacing failure modes, tool misuse, and reasoning errors.
Experimentation
Compare prompts, models, and configurations across datasets with side-by-side outputs.
Production Monitoring
Real-time alerts, tracing, and dashboards for live LLM applications.
Dataset Generation
Synthetic dataset creation including red-teaming sets for RAG and agent systems.
Human Annotation
Workflows for human-in-the-loop labeling and reviewer agreement tracking.
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Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
RAG Evaluation
Score retrieval and generation quality in RAG applications across faithfulness, relevance, and context.
Agent Debugging
Trace and diagnose failures in multi-step agentic systems using Percival.
Model Benchmarking
Benchmark candidate models against domain-specific datasets such as FinanceBench.
Guardrails
Apply Patronus judges as runtime guardrails on LLM responses.
Regression Testing
Detect quality regressions across prompt, model, and configuration changes.
Integrations 6
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
OpenAI
Score outputs from OpenAI models inside Patronus experiments and monitoring.
Anthropic
Evaluate Anthropic Claude outputs using Patronus judges.
LangChain
SDK integrations for LangChain chains and agents.
LlamaIndex
Evaluate LlamaIndex RAG pipelines with Patronus evaluators.
OpenTelemetry
Ingest OTel-compatible LLM traces for evaluation and monitoring.
Hugging Face
Lynx and Glider weights are distributed via Hugging Face for self-hosting.
Resources
Every other property we hold for Patronus AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 1
Properties that don't map to a standard resource type
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