LangWatch

LangWatch is an open-source engineering platform for building dependable AI agents, combining pre-release simulation with production evaluation and tracing. It suits development teams, QA, product owners, and domain experts who need one workflow for agent quality and operations. The main trade-off is that its free allowance is better for small pilots than ongoing, multi-user production workloads.

Overview

LangWatch covers the agent lifecycle from development through live traffic. Simulations exercise multi-turn conversations, tool use, recovery behavior, and business outcomes, so teams can assess workflows that ordinary single-prompt tests miss. Evaluations run offline on datasets, continuously on production traffic, or in CI/CD to block merges when quality checks fail.

Its observability traces agent steps and reports cost, token use, duration, errors, evaluation results, and annotations. Cost and token tracking, LLM and agent tracing, retrieval tracing, and prompt management make it relevant to teams that need both quality evidence and operational accounting. The project is open source under Apache 2.0, and LangWatch B.V. is based in Amsterdam.

Key features

• Agent simulations: Multi-turn, tool-use, recovery, and outcome scenarios provide a stronger pre-launch check for agents whose failures emerge only across a sequence of actions.
• Evaluation workflows: Dataset, production, and CI/CD modes let developers and reviewers use the same quality process before and after release.
• Tracing and cost visibility: Step-level traces with tokens, duration, errors, evaluations, annotations, and cost help teams diagnose regressions and understand spend.
• Integrations: Python and TypeScript SDKs, OpenTelemetry, REST ingestion, model providers, and frameworks such as LangGraph, LangChain, CrewAI, and DSPy reduce the need to redesign an existing stack.
• Voice testing: Support for OpenAI Realtime, Gemini, ElevenLabs, Google Live API, Pipecat, WebSocket bots, and Twilio extends testing to voice-agent workflows.
• Security and privacy: AES-256 at rest, TLS 1.2+ in transit, AWS KMS, RBAC, MFA, and SSO options address enterprise controls. Automatic PII detection and removal, GDPR right-to-be-forgotten support, and enterprise DPAs strengthen data-handling coverage.

Pricing

Developer: Free forever at €0.00 per user. It includes 50,000 events per month, 14-day data access, two users, three scenarios, three simulations, and three custom evaluations, with community support through GitHub and Discord. This is suitable for an individual developer or a small proof of concept; the short retention and tight scenario quotas constrain sustained team operations.

Growth: €29.00 per core-seat per month. The plan includes 200,000 events, then charges €5 per additional 100,000 events, with 30-day retention and extension at €3 per GB. Lite users, simulations, evaluations, and prompts are unlimited, and private Slack or Teams support replaces community-only assistance. It fits a team running regular evaluation and production monitoring, but event overages and retention extensions should be budgeted.

Enterprise: Custom pricing. Hybrid, self-hosted, or on-premises deployment comes with custom retention, SSO/RBAC, audit logs, SLAs, ISO 27001 reports, information-security and legal review, and custom terms and DPA. This is the appropriate route for organizations with formal compliance, contractual, or infrastructure requirements.

Platforms

LangWatch is offered through an API, Linux, web access, and self-hosted deployment. Managed cloud, hybrid, and self-hosted choices provide infrastructure flexibility; self-hosting uses Docker Compose and the customer's ClickHouse, so it requires operational ownership rather than a turnkey service.

Who it's for

Choose LangWatch if your AI team needs to test agent behavior across conversations and tools, gate releases with evaluations, and inspect production traces in the same system. It is particularly suitable for teams using supported Python or TypeScript stacks and for organizations that need cloud, hybrid, or on-premises deployment. A solo builder can start on Developer, while teams with regular traffic will reach Growth's quotas quickly. Readers wanting a very small, permanently free monitoring footprint may find the Developer limits restrictive.

Pros and cons

Pros

• Simulation covers multi-turn behavior, tool use, recovery, and business outcomes rather than only isolated responses.
• Offline, production, and CI/CD evaluations support a continuous quality process.
• Open-source Apache 2.0 licensing and three deployment models accommodate infrastructure and data-governance preferences.
• Voice-agent coverage and broad framework ingestion make it useful beyond text-only agents.
• Growth removes limits on lite users, simulations, evaluations, and prompts.

Cons

• Developer retention is only 14 days, with two users and three-item scenario, simulation, and evaluation caps.
• Growth charges for event overages and additional retention, making usage volume a direct cost driver.
• Self-hosting requires Docker Compose and customer-managed ClickHouse.
• Enterprise capabilities and pricing require a custom engagement.

Alternatives

For broader category browsing, see AI LLM Observability Tools, LLM Observability Tools, Model Monitoring Software, AI Agent Observability Tools, AI Agent Evaluation Tools, and AI LLM Evaluation Tools.

W&B Weave is a fit when you want hosted evaluations and tracing with a free tier measured at 1 GB of monthly ingestion and 5 GB of storage across more desktop platforms. Choose OpenLIT for unlimited self-hosted usage, users, projects, and environments under its free OSS plan. Opik suits teams seeking open-source observability and evaluation code they can run locally. Agenta is an option for a small team whose free Hobby plan allows two members, 5,000 agent runs per month, and 20 evaluations per month. Portkey fits lighter logging needs with 10,000 recorded logs monthly and three-day log retention on its free Developer plan. Braintrust offers a free Starter allowance of 1 GB processed data, 10,000 scores, 14-day retention, and unlimited users and projects. Confident AI is better for a very small evaluation setup with two seats, one project, and five test runs per week on Free. Fiddler AI is an alternative for teams prioritizing real-time guardrails and AI observability.

Verdict

LangWatch is a strong choice for AI teams that want agent simulation, evaluation gates, and production tracing joined in one open-source platform. Pick it when deployment control and multi-stage agent quality matter; look elsewhere if the Developer quotas are too small or you want to avoid managing ClickHouse in a self-hosted setup.