Judgeval vs Temporal AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-26
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Judgeval or Temporal AI: which should you choose?

DimensionJudgevalTemporal AI
PricingContact for pricingFreemium (usage-based billing for cloud)
Primary Use CaseContinuous monitoring and improvement of AI agents in productionDurable execution for reliable workflows and AI agents
Key DifferentiatorAgent swarm triage and Slack-native failure detectionAutomatic state capture and recovery for long-running processes
IntegrationsSlack, LangSmithOpenAI Agents SDK, Google ADK, Slack, Twilio, Kubernetes, Azure
Best ForAI engineering teams debugging production agent failuresTeams building fault-tolerant workflows and AI agents

Choose Temporal if you need a durable execution engine to build reliable agents and workflows that survive failures—it's proven by companies like OpenAI. Choose Judgeval if your agents are already in production and you need a continuous improvement loop to detect, triage, and fix issues at scale with minimal overhead. They complement each other: Temporal builds reliability in; Judgeval keeps it there.

Judgeval
Judgeval

Slack-native continuous improvement for production AI agents: triage failures, isolate root causes, validate fixes before you ship.

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Temporal AI
Temporal AI

Durable execution platform that keeps AI agents and long-running workflows alive through crashes, retries, and abandoned sessions.

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Pricing
Contact Sales
Freemium
Plans
—
$0 ($150 credits for 90 days)
Starting at $50 per million actions
Greater of $500/mo or 10% of usage
Custom
Popularity
6 views
7.5k views
Skill Level
Advanced
Advanced
API Available
Platforms
WebAPIPluginDesktop
WebAPI
Categories
📡 LLM Observability & Evals
🕸️ Agent Frameworks & Orchestration⚙️ Developer Infrastructure
Features
Slack-native agent failure investigation via @Judgment mentions
Agent swarm triage to find similar failure cases across production traces
Root cause analysis with dollar impact and occurrence counts
Affected-customer identification including top-account hits
Behavior tracking with automatic recurrence alerts
Detection-rate views over a configurable window (7-day demo over 1.3K traces)
Missed escalation detection and refund overrun monitoring
Production trace replay to test proposed fixes
Compare Runs to validate fixes against production cases before deployment
Agent Judge framework for long-horizon task evaluation
Agentic judges that search, verify, and adapt to evaluate agent output
False-positive checking on candidate failure matches
Custom eval criteria authoring via Agent Judge
Trace filtering and drill-down across failed and passing sessions
Blast-radius quantification by session count and percentage of runs
Durable execution captures Workflow state at every step with no checkpointing or recovery code
Native SDKs for Go, Java, Python, TypeScript, .NET, PHP, Ruby, and Rust
Activities retry automatically with backoff, four timeout classes, and heartbeating
Signals, Queries, and Updates read and mutate running Workflows mid-flight
Workflow Streams for real-time interactivity with running executions
Durable AI agents via OpenAI Agents SDK and Google ADK running LLM and tool calls as Activities
Standalone Activities as a durable job-queue pattern, GA across six SDKs (2026-09-15)
Serverless Workers for Google Cloud Run, public preview (2026-10-05)
Serverless Workers for AWS Bedrock AgentCore, pre-release (2026-09-18)
Native Task Queue priority and fair distribution without a custom queueing layer
Durable Timers that sleep for months; cron Schedules with backfill and Continue-As-New
Saga pattern via compensating transactions that read like try/catch
Worker Versioning pins Workflows to a version so deploys don't corrupt in-flight runs
Cloud UI Strict Session Mode: 15-minute inactivity timeout, 12-hour max session (GA)
Replay tests validate against real histories; time-skipping tests fast-forward long timers in CI
Integrations
Slack
LangSmith
OpenAI Agents SDK
Google ADK
AWS Lambda
Google Cloud Run
Amazon Bedrock AgentCore
Kubernetes
GitHub Actions
GCP Marketplace
Azure

Who should pick Judgeval, and who should pick Temporal AI?

  • Developer building AI agents from scratch
    Pick: Temporal AI

    Temporal provides the durable execution foundation needed to build agents that survive failures, with multiple SDK choices and automatic recovery.

  • AI ops team managing production agents
    Pick: Judgeval

    Judgeval's Slack-native triage and automated root cause analysis are purpose-built for monitoring and improving deployed agents quickly.

  • Platform team building microservices orchestration
    Pick: Temporal AI

    Temporal's workflow engine with retries, timeouts, and Saga pattern is ideal for orchestrating multi-step services and compensating transactions.

  • Company looking to reduce agent failure rates
    Pick: Judgeval

    Judgeval's behavior tracking and recurrence alerts help identify and fix root causes, reducing failure rates over time with data-driven insights.

  • Startup building AI features with limited budget
    Pick: Temporal AI

    Temporal's open-source and freemium cloud offering make it cost-effective for building reliable workflows without upfront investment.

Frequently Asked Questions

Judgeval vs Temporal AI: which should you choose?

Choose Temporal if you need a durable execution engine to build reliable agents and workflows that survive failures—it's proven by companies like OpenAI. Choose Judgeval if your agents are already in production and you need a continuous improvement loop to detect, triage, and fix issues at scale with minimal overhead. They complement each other: Temporal builds reliability in; Judgeval keeps it there.

Can Temporal and Judgeval be used together?

Yes, they are complementary: Temporal ensures durable execution and automatic recovery, while Judgeval monitors and improves agent behavior in production. You could build agents on Temporal and use Judgeval to detect failures and validate fixes.

Does Judgeval require Temporal to work?

No, Judgeval is independent. It integrates with LangSmith and Slack, and can work with any agent framework. It focuses on analyzing traces and failures regardless of the underlying orchestration.

Which is better for long-running workflows?

Temporal is designed for long-running workflows with automatic state capture, persistence, and recovery. Judgeval evaluates long-horizon agents via Agent Judge but does not execute them.

Which has easier onboarding?

Temporal offers SDKs in many languages and a freemium tier, making it easy to start. Judgeval requires contacting sales, which may involve a longer onboarding process.

Can Judgeval detect failures automatically?

Yes, it detects agent failures via Slack integration and automated trace analysis, using an agent swarm to root-cause issues.

Does Temporal support human-in-the-loop workflows?

Yes, Temporal has human-in-the-loop via signals and pause/resume, allowing manual intervention during workflow execution.

Is Judgeval suitable for small teams?

Judgeval is best for teams with production agent deployments that need dedicated reliability tooling. Small teams with simple chatbots may not need its advanced triage features.

What is the latest pricing model for Temporal?

Temporal recently introduced usage-based billing for better cost transparency, alongside its free tier and open-source option.

What else are Judgeval and Temporal AI compared with?

Where can you read more about Judgeval and Temporal AI?

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Last reviewed: July 6, 2026