Master Humanloop with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Create an Anthropic Console account Sign up at console.anthropic.com to access the platform where Humanloop's technology now lives as native features. Navigate to the Workbench tab Open the Workbench in Anthropic Console to begin prompt engineering with version control, branching, and A/B testing capabilities inherited from Humanloop. Set up your first Evaluation Use the Evaluations tab to define success criteria for your Claude application and run automated grading across test cases — this is the core Humanloop IP integrated into the Console. Configure human feedback workflows (Enterprise) For enterprise accounts, set up structured review interfaces where domain experts can provide feedback on model outputs, enabling continuous improvement cycles.
💡 Quick Start: Follow these 1 steps in order to get up and running with Humanloop quickly.
Explore the key features that make Humanloop powerful for developer workflows.
Interactive environment now native to Anthropic Console where developers version-control prompts, run A/B tests between model versions, and collaborate on prompt development with branching and merging workflows similar to Git for code. Supports inline diff views and staged rollouts to production.
Product and engineering teams iterating on different prompt variations for a customer support chatbot, testing Claude Sonnet vs Opus with staged rollouts and performance tracking across thousands of real conversations.
Humanloop's core IP and the primary reason for the Anthropic acquisition. Allows teams to define success criteria (JSON format compliance, tone empathy, factual accuracy) and automatically grade thousands of model outputs against these rules using LLM-as-judge or programmatic evaluators.
Enterprise teams running regression tests when upgrading from Claude Sonnet 3.5 to Sonnet 4, ensuring answer quality doesn't degrade across 10,000+ test cases before promoting the new model to production traffic.
Streamlined interface for domain experts (lawyers, doctors, compliance officers) to provide structured feedback on model outputs, which feeds into fine-tuning datasets and continuous improvement workflows. Includes inter-rater reliability tracking and disagreement resolution.
Medical professionals reviewing AI-generated patient summaries at AstraZeneca-style deployments and providing corrections that are automatically formatted into fine-tuning datasets for domain-specific model improvement.
Centralized library treating prompts as code with full version history (v1.2, v1.3), rollback capability, and deployment management — ensuring bad prompt updates can always be reverted in seconds rather than requiring code deploys. Includes change-attribution and approval workflows.
Managing production prompts across a team of 20 developers at a company like Gusto or Vanta, with clear ownership, change tracking, and the ability to instantly roll back if a prompt update causes quality regressions detected in production monitoring.
Real-time tracking of LLM application performance including cost metrics, latency, quality scores, and user feedback collection with automated alerting on quality degradation. Integrates directly with the Evaluations system for online evaluation of live traffic samples.
Monitoring a customer-facing Claude assistant for response quality trends, catching and alerting on quality drops within minutes before they impact user satisfaction metrics or trigger support escalations.
Humanloop was acquired by Anthropic in 2025 after operating independently for approximately five years and raising $10.7 million in venture funding. The standalone platform was subsequently sunsetted, and the team and technology were integrated into the Anthropic Console. Humanloop's features now exist as the Workbench and Evaluations tabs within Anthropic's enterprise suite, accessible to Claude API customers. Co-founders Raza Habib, Peter Hayes, and Jordan Burgess joined Anthropic as part of the deal.
Yes, but only through Anthropic's platform. The Workbench (prompt engineering with version control and A/B testing), Evaluations (automated grading against custom criteria), and human feedback workflows are now native features of the Anthropic Console. You'll need an Anthropic API account to access them, and some advanced enterprise features may require a custom Anthropic enterprise agreement. The legacy Humanloop SDK has been deprecated.
Based on our analysis of 870+ AI tools, the top three model-agnostic alternatives are LangSmith (from LangChain, with the largest community at 100K+ developers), Langfuse (open-source with self-hosting, used by 5,000+ teams), and Weights & Biases Weave (best for ML-mature teams already using W&B). LangSmith pricing starts at $39/user/month, Langfuse offers a generous free tier plus paid Cloud and Enterprise plans starting at $59/month, and W&B offers free personal accounts. All three support Claude, GPT-4, Gemini, and open-source models — preserving the multi-provider flexibility Humanloop offered before the acquisition.
Anthropic acquired Humanloop to gain the industry's most mature evaluation infrastructure and the team that built it. The acquisition addressed the gap between having capable models and providing enterprises with the tooling to measure, test, and trust AI outputs — essentially adding 'enterprise readiness' to Anthropic's offering for Fortune 500 clients. Humanloop's customer base of Duolingo, Gusto, Vanta, and AstraZeneca also provided Anthropic with direct relationships into key enterprise accounts. The acqui-hire reflected a broader trend of model providers absorbing tooling layers rather than partnering with them.
If you were a Humanloop customer and don't want to commit to Anthropic, the most direct migration path is to LangSmith or Langfuse, both of which offer documentation for onboarding from other LLMOps platforms. Export your prompt registry and evaluation datasets, then import the JSON-formatted prompts and test cases into the new platform. Evaluator criteria typically require manual reconfiguration, since each platform uses a different DSL for grading rules. Budget approximately one to two engineering weeks per production application for full migration.
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Tutorial updated March 2026