Judgment

What it does

Judgment is an agent-monitoring and improvement platform for teams building and operating long-horizon AI agents. Instrument an agent in Python or TypeScript to send traces containing its root activity, model calls, tool calls, inputs, outputs and session context to a project. Framework or model-provider integrations can capture spans automatically; custom spans can be added where needed. Teams can inspect trace trees, search for recurring behavior, and define signals using filters, code checks, decision models or agentic judges. Background agents can surface issues found in production traces.

The platform also supports offline evaluation: teams can run tests, compare judge results and examine regressions before release. Issue alerts, scheduled analysis tasks and agent access through MCP or Slack are documented. It is aimed at agent-native developers and teams responsible for agent quality and production monitoring; access is through the hosted web platform, SDKs, MCP and Slack. Signup is offered directly, with a demo option for sales-led plans. Tracing setup requires a project, organization ID and API key; supported SDK setup uses Python 3.10+ or Node 18+.

Beginner has no subscription fee and includes a recurring monthly credit allowance plus limited trace ingestion and evaluator runs. Pro adds higher allowances and longer retention, while Enterprise supports customer-cloud deployment. Model usage beyond credits, additional ingestion and excess evaluator runs are charged separately. Correct instrumentation matters: duplicate spans can distort cost data, and queued traces can be lost when a process exits.

What it can do

• Capture and inspect agent traces, including model calls, tool calls and session context
• Search traces with structured and semantic filters
• Detect recurring production issues with configurable signals and background agents
• Monitor using filters, code checks, decision models and agentic judges
• Run offline evaluations and inspect test results
• Schedule agent analysis tasks and use Judgment through MCP or Slack

Works with

• OpenAI
• Anthropic
• Together
• Google GenAI
• LangGraph
• LangChain
• OpenAI Agents SDK
• Claude Agent SDK
• Vercel AI SDK
• OpenTelemetry

Before you choose

• Model usage is billed at provider rates after plan credits are used; ingest and evaluator runs also have metered allowances and overage rates.
• SDK tracing setup requires correct initialization and span handling; duplicate framework and provider instrumentation can record model spans, tokens and costs twice.
• The SDK batches spans; traces still queued when a process stops may be lost. Enterprise is required for bring-your-own-cloud deployment.

Listed under

• Coding
• MCP and agent tools
• Agent monitoring
• AI agents
• Continuous improvement
• Customer support agents
• Debugging
• Evals

Get started

The site links to signup; a demo is also available.

1. Create an account and choose a Judgment project and deployment environment.
2. Install the SDK: Python 3.10+ with `pip install judgeval openai`, or Node 18+ with `npm install judgeval openai`.
3. Set `JUDGMENT_API_KEY` and `JUDGMENT_ORG_ID`, instrument an agent path, then verify its first trace.

The pricing page states monthly plan amounts. Credits and tokens are billed at model-provider rates once plan credits are used up. Additional trace ingest and evaluator runs are charged by usage. Enterprise has custom pricing and volume rates.

Source: judgmentlabs.ai/pricing, checked 1 Nov 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.

• Judgment Labs — Find and fix what your agents get wrong ↗www.judgmentlabs.ai
• Set up tracing | Judgment Docs - Judgment Labs ↗docs.judgmentlabs.ai
• Judgment Labs — Pricing ↗www.judgmentlabs.ai
• Judgment Labs — Find and fix what your agents get wrong ↗www.judgmentlabs.ai

Company website

judgmentlabs.ai: product, pricing and policy pages

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Human review

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Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.