AI Frameworks · langwatch

langwatch

LangWatch is an open-core platform for testing, evaluating, and monitoring LLM-powered agents. It provides end-to-end agent simulations, evaluation workflows, observability, and an AI gateway with cost control—deployable on your own infrastructure or via cloud.

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Source: GitHub — github.com/langwatch/langwatch

3.3k

GitHub stars

327

Forks

TypeScript

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	langwatch/langwatch
Owner	langwatch
Primary language	TypeScript
License	Apache-2.0 — OSI-approved
Stars	3.3k
Forks	327
Open issues	674
Latest release	langwatch-3.5.0 (2026-06-29)
Last updated	2026-07-07
Source	https://github.com/langwatch/langwatch

What langwatch is

TypeScript-based observability and evaluation platform built on OpenTelemetry/OTLP standards. Includes a Go-based AI gateway (~700 ns overhead) with provider-agnostic routing, virtual keys, and hierarchical budgets; traces dataset generation; LLM-based or custom evaluators; and integrations with LangChain, LangGraph, Vercel AI SDK, CrewAI, and major model providers.

Quickstart

Get the langwatch source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/langwatch/langwatch.git2cd langwatch3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Agent regression testing and scenario validation

Run realistic end-to-end simulations against your full agent stack (tools, state, user simulator, judge) before and after production deploys to catch breakage early.

LLM observability and cost governance

Monitor traces in production, control spend via hierarchical budgets and virtual keys, apply guardrails, and route across providers (OpenAI/Anthropic) with automatic fallback.

Evaluation-driven prompt optimization

Capture traces into datasets, run evaluations, optimize prompts/models, and re-test—all in one loop without external tool sprawl or custom glue code.

Implementation considerations

Self-hosted deployments require standing up PostgreSQL, Redis, and ClickHouse; Docker Compose or Kubernetes (Helm) charts provided but still require ops overhead.
OpenTelemetry-native design means you export traces via standard OTLP; integration with frameworks (LangChain, LangGraph, Vercel AI) is supported but requires SDK setup.
Evaluation logic can be custom or LLM-based; design your scoring rubrics and ground truth datasets upfront to avoid rework.
AI Gateway is optional but recommended for cost control; requires separate Go binary deployment and routing configuration if not using cloud.
Hybrid data residency (OnPrem data option) exists but requires clarification from vendor on architecture and setup costs.

When to avoid it — and what to weigh

Single-model, non-agent use cases — If you're only calling an LLM once per request with no agent logic or tool orchestration, simpler observability tools may suffice.
Minimal deployment footprint required — Self-hosted LangWatch requires PostgreSQL, Redis, ClickHouse, and a Go binary—non-trivial infrastructure if you want zero additional services.
Vendor lock-in constraints with existing platforms — LangWatch is framework-agnostic but ties workflows to its UI/API; teams deeply integrated with other eval or observability platforms may face migration friction.
Real-time latency-critical applications — Observability platform overhead and async evaluation may not suit sub-10ms response-time requirements; gateway adds ~700 ns but full tracing pipeline latency unknown.

License & commercial use

Apache License 2.0 (permissive OSI license) is the stated floor. Badge indicates 'Apache 2.0 + Enterprise' suggesting commercial or proprietary extensions exist for some features.

Apache 2.0 permits commercial use of the open-source core without royalties. However, the README badge 'Apache 2.0 + Enterprise' suggests certain features (e.g., advanced security, higher SLA, managed hosting) may require separate Enterprise licensing. Requires clarification from vendor on which features are open-core vs. enterprise-only and applicable commercial terms.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Needs review
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

OpenTelemetry standard provides transport security baseline. AI Gateway supports virtual keys and hierarchical budgets for cost/access control. Self-hosted deployments inherit security posture of PostgreSQL, Redis, ClickHouse, and network configuration. Data residency options (OnPrem, Hybrid) available for regulated workloads. No security audit, penetration test results, or CVE history provided in source data; independent review recommended before handling sensitive data.

Alternatives to consider

Weights & Biases (W&B)

Broader ML observability and experiment tracking; stronger for multi-model tuning but less agent-simulation focused.

Arize AI / Giskard

Model monitoring and evaluation platforms; Giskard emphasizes open-source; neither has integrated AI Gateway or cost control layer.

Custom observability (OpenTelemetry + Prometheus + custom dashboards)

Maximum control and no platform lock-in; requires significant engineering effort and sacrifices LLM-specific workflows (agent simulation, eval, prompt optimization).

Software development agency

Build on langwatch with DEV.co software developers

Start with LangWatch cloud (free tier) or self-host on your infrastructure. Review the open-source features vs. Enterprise licensing with the team before production rollout.

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langwatch FAQ

Can I use LangWatch with models from providers other than OpenAI?

Yes. LangWatch is framework- and provider-agnostic by design. It supports Anthropic, Azure, Google Cloud, AWS, Groq, Ollama, and any OpenTelemetry-compatible library. The AI Gateway supports OpenAI and Anthropic natively; integration depth for other providers unknown.

What infrastructure do I need to self-host LangWatch?

At minimum: PostgreSQL, Redis, ClickHouse, Node.js runtime, and (for AI Gateway) a Go binary. Docker Compose or Kubernetes (Helm) configurations are provided. Local-only setup requires only Node.js (uv, postgres, redis, clickhouse are downloaded into ~/.langwatch/).

Is the open-source version sufficient, or do I need Enterprise?

README states 'Apache 2.0 + Enterprise' but does not clearly delineate which features require paid licensing. Recommend reviewing feature matrix with vendor or running pilot on open-source before committing.

Does LangWatch add latency to my agent requests?

The AI Gateway claims ~700 ns overhead on the hot path. Full observability pipeline latency (trace export, database writes) is not quantified; test with representative workloads in staging.

Software developers & web developers for hire

DEV.co helps companies turn open-source tools like langwatch into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai frameworks stack.

Ready to test your agents reliably?

Start with LangWatch cloud (free tier) or self-host on your infrastructure. Review the open-source features vs. Enterprise licensing with the team before production rollout.

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