LangWatch is a comprehensive LLM engineering platform designed for teams that need to ship AI agents to production with confidence. It provides a complete end-to-end suite for agent simulation, testing, evaluation, observability, and governance. By enabling developers to move beyond basic unit tests to realistic, simulation-based testing, LangWatch helps transition unpredictable AI prototypes into reliable, production-ready systems. The platform includes an automated AI engineer called Langy, which can read production traces, write tests, open pull requests, and prove fixes directly in your CI pipeline, significantly reducing the engineering time required to fix agent regressions.

Functionality includes tools for agentic AI testing that runs realistic scenarios, LLM evaluation to measure response quality, observability to trace every agent step, and a governance gateway for managing keys, budgets, and routing policies. It is designed to work with all popular agent frameworks and LLM providers.

Some of the key features are:

• Langy: An automated AI engineer that analyzes production traces to identify problems and generates code-based fixes via pull requests.
• Simulation Testing: Runs multi-turn conversations and adversarial red-teaming scenarios against agents to catch issues before deployment.
• LLM Evaluation: Offers a comprehensive suite of evaluators for faithfulness, relevancy, correctness, and safety, with support for custom metrics.
• Observability: OpenTelemetry-native tracing that provides full visibility into every token, tool call, and latency metric across the entire agent lifecycle.
• AI Governance: Provides a centralized gateway to manage API keys, budgets, and routing policies with full audit logging for compliance.
• Prompt Management: Treat prompts as versioned code, enabling seamless integration with GitHub workflows, A/B testing, and rapid rollback capabilities.
• Voice AI Support: Allows simulation and testing of voice agents, including handling noise, interruptions, and latency in a realistic manner.
• Cost Attribution: Tracks token usage and theoretical spend across different models and teams, enabling granular budget control.

LangWatch integrates into your existing development lifecycle as a standard part of your CI/CD pipeline. By connecting your agent to the LangWatch SDK or CLI, you gain the ability to run simulations on every pull request, monitor production traffic for quality drift, and manage prompt versions as source-controlled files. The platform is designed to be framework-agnostic, supporting Python, TypeScript, and Go, and is compatible with any agent framework that supports OpenTelemetry, allowing for deployment in cloud, self-hosted, or hybrid environments.

Some common use cases include:

• Debugging Agent Regressions: Turning a failed production conversation into a persistent simulation test case that prevents future regressions.
• Adversarial Red-Teaming: Automating 50-turn crescendo attacks to proactively find jailbreaks, system prompt extractions, and data exfiltration paths.
• Production Monitoring: Real-time evaluation of production traffic to trigger alerts whenever agent quality or policy adherence drops below a defined threshold.
• Enterprise AI Governance: Enforcing cost controls and security policies on AI tool usage across teams by using virtual, rotatable keys.