LangWatch
LLM observability, evaluation and agent simulation testing platform
What is LangWatch?
LangWatch is a platform for tracing, evaluating and testing LLM applications and AI agents. It offers trace-level observability with cost and latency tracking, LLM evaluations, simulated user scenarios for agent testing, prompt management with GitHub sync, an AI gateway and red-teaming. Usage tracking for coding agents such as Claude Code and Codex is one feature among many, and the platform can be self-hosted. (An earlier version of this page described a different product, which was incorrect.)
Score breakdown (out of 5)
What you would use it for
- →Monitoring LLM apps in production
- →Testing AI agents before release
Pros & Cons
👍 Pros
- ✓Covers observability, evals and testing in one platform
- ✓Self-hostable with a free developer tier
👎 Cons
- ✗Priced per core seat on paid plans
Key Features
- ✓ Trace-level observability with cost and latency tracking
- ✓ LLM evaluations and simulated user scenarios
- ✓ Prompt management with GitHub sync
- ✓ AI gateway and red-teaming
- ✓ Usage tracking for coding agents
Available on
Integrates with
LangWatch Pricing
✅ LangWatch has a free plan - no credit card required to start.
Developer
- ✓50k events/month
- ✓14-day data access
- ✓2 users
- ✓3 scenarios, 3 simulations, 3 custom evals
Growth
- ✓200k events included, then €5 per 100k
- ✓30-day retention (extend at €3/GB)
- ✓Unlimited lite users
- ✓Unlimited simulations, evals, prompts
From the blog
Nvidia releases safety watchdog. The chip polices runaway AI agents.
Nvidia has unveiled a dedicated safety chip designed to monitor and control autonomous AI agents, preventing them from operating outside intended parameters. The watchdog hardware aims to address growing concerns about AI system reliability as autonomous agents become more prevalent.
Sep 29, 2026
AI agents can now pay for things online. Here's what can go wrong.
A live protocol called x402 lets AI agents autonomously purchase web content using crypto payments with zero human approval. A developer explores the security risks and built tools to catch malicious spending patterns.
Sep 6, 2026
Researchers extract hidden reasoning from LLM APIs. Proprietary model internals are leaking.
Security research reveals methods to extract internal reasoning traces and model capabilities from proprietary LLM APIs, raising concerns about intellectual property theft and prompt injection vulnerabilities.
Aug 17, 2026
Developer resources
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