LangWatch: LLM observability and analytics platform for monitoring AI agent quality, costs, and user experience with real-time dashboards and automated guardrails.
Monitor your AI's quality and costs in production — catch issues, track spending, and understand how users interact with your AI.
LangWatch is an Analytics & Monitoring observability platform that helps engineering teams test, evaluate, and monitor LLM applications and AI agents in production, with pricing starting free and paid plans available for growing teams. Built for AI engineers, product managers, and compliance teams shipping production-grade generative AI features.
Founded in 2023 and headquartered in Amsterdam, LangWatch provides an OpenTelemetry-native tracing layer that captures every prompt, completion, tool call, retrieval step, and metadata point flowing through your agent stack. The platform layers automated evaluations, real-time guardrails, and conversation analytics on top of that tracing foundation, giving teams a single pane of glass for quality, safety, and cost management across their entire LLM infrastructure.
The tracing system auto-instruments popular frameworks including LangChain, LlamaIndex, DSPy, Haystack, and the Vercel AI SDK through lightweight Python and TypeScript SDKs. Because the instrumentation follows the OpenTelemetry standard, teams can forward the same spans to existing observability backends like Datadog or Grafana without maintaining separate pipelines. Each trace captures the full execution graph of an agent run — from the initial user message through retrieval, tool invocations, and the final completion — along with token counts, latencies, and cost breakdowns at every step.
On the evaluation side, LangWatch runs continuous quality checks against production traces using both deterministic rules and LLM-as-a-judge scoring methods. Teams can measure faithfulness, relevance, helpfulness, sentiment, and custom domain-specific metrics, with failed evaluations triggering alerts, routing conversations to human review queues, or gating deployments through CI/CD integration. The Simulation & Testing Suite extends this by replaying synthetic and recorded conversations against different agent versions, enabling regression testing before changes reach users.
Real-time guardrails distinguish LangWatch from tracing-only platforms. Policy checks — PII detection and redaction, toxicity filtering, topic adherence enforcement, jailbreak detection, and custom validation rules — can run synchronously to block problematic responses or asynchronously to flag them for later review. This dual mode lets teams balance response latency against safety strictness on a per-rule basis.
The Optimization Studio, powered by Stanford's DSPy framework, automates prompt tuning by searching for optimal prompt configurations, few-shot examples, and pipeline parameters against user-defined evaluation metrics. Rather than manual iteration, engineers define what good output looks like and let the system discover prompt strategies that often outperform hand-tuned baselines.
LangWatch offers a generous free Developer tier suitable for prototyping and small production workloads, a Launch tier starting at $200/month for scaling teams, and custom Enterprise pricing that unlocks self-hosted deployment, SSO, audit logs, and dedicated SLAs. The platform's EU-hosted infrastructure and compliance documentation covering GDPR, ISO 27001, and SOC 2 make it a strong fit for regulated industries in finance, healthcare, and government.
Was this helpful?
Captures full execution traces of every agent run — prompts, completions, tool calls, retrieval steps, latency, and token costs — through Python and TypeScript SDKs with auto-instrumentation for 20+ frameworks. Because tracing is built on the OpenTelemetry standard, teams can pipe the same spans to existing observability stacks like Datadog or Grafana alongside LangWatch, avoiding vendor lock-in.
Applies configurable policy checks — PII detection and redaction, toxicity filtering, topic adherence, jailbreak detection, response length limits, and custom validation rules — to LLM outputs before they reach end users. Checks can run synchronously to block bad responses or asynchronously to flag them for review, letting teams balance latency against safety on a per-rule basis.
Runs continuous quality evaluations on production traces using both rule-based checks and LLM-as-a-judge methods, scoring metrics like faithfulness, relevance, helpfulness, and sentiment. Failed evaluations can trigger alerts, route conversations to human review queues, or block deployments via CI/CD integration.
Lets teams replay synthetic and recorded conversations against different agent versions to benchmark behavior changes before shipping. This is particularly valuable for multi-agent systems where prompt edits in one component can have non-obvious downstream effects, and it integrates with CI to gate releases on regression thresholds.
Uses Stanford's DSPy framework under the hood to automatically tune prompts, few-shot examples, and pipeline configurations against your evaluation dataset. Instead of manually iterating on prompts, engineers define metrics and let the studio search for optimal configurations, often surfacing prompt improvements that hand-tuning would miss.
$0/month
Starts at $200/month
Custom
Ready to get started with LangWatch?
View Pricing Options →LangWatch works with these platforms and services:
We believe in transparent reviews. Here's what LangWatch doesn't handle well:
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
Recent platform updates emphasize the Optimization Studio powered by DSPy for automated prompt tuning, expanded simulation testing for multi-agent systems, and deeper OpenTelemetry compatibility for piping LangWatch traces into existing observability stacks. The platform continues to expand its evaluator library, including LLM-as-a-judge templates for RAG faithfulness and agent task completion.
LLM Observability
Langfuse is an open-source LLM observability and engineering platform providing tracing, prompt management, evaluations, and dataset management for production AI applications.
LLM Observability
Open-source LLM observability and AI gateway — logs every prompt, response, cost, and latency across 20+ providers with a one-line proxy or async SDK, plus caching, retries, and prompt experiments.
Analytics & Monitoring
Langtrace: Open-source observability platform for LLM applications and AI agents with OpenTelemetry-based tracing, cost tracking, and performance analytics across 8+ model providers and 10+ frameworks.
Enterprise Agents
Developer platform for AI agent observability, debugging, and cost tracking with two-line SDK integration.
No reviews yet. Be the first to share your experience!
Get started with LangWatch and see if it's the right fit for your needs.
Get Started →Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →