Trusted by AI innovators & global enterprise

Trusted by AI innovators & global enterprise

Trusted by AI innovators & global enterprise

Nikhil Nagaraj

CTO - Struck

"LangWatch is helping us understand what users are asking and if the actual answer provided is correct. It's great to evaluate our RAG application with the evaluations setup and knowing what users like / dislike. Thanks so much for all the support"

Nikhil Nagaraj

Nikhil Nagaraj

CTO - Struck

"LangWatch is helping us understand what users are asking and if the actual answer provided is correct. It's great to evaluate our RAG application with the evaluations setup and knowing what users like / dislike. Thanks so much for all the support"

Nikhil Nagaraj

Nikhil Nagaraj

CTO - Struck

"LangWatch is helping us understand what users are asking and if the actual answer provided is correct. It's great to evaluate our RAG application with the evaluations setup and knowing what users like / dislike. Thanks so much for all the support"

Nikhil Nagaraj

AI evaluations, automated - from dev to production

Test your agents and prevent regressions

Run Realtime and Offline Evaluations

Integrate any model and start evaluating with live production data or offline datasets. Track metrics over time, debug regressions instantly, and ensure consistent performance from development to deployment.

Run Realtime and Offline Evaluations

Integrate any model and start evaluating with live production data or offline datasets. Track metrics over time, debug regressions instantly, and ensure consistent performance from development to deployment.

Run Realtime and Offline Evaluations

Integrate any model and start evaluating with live production data or offline datasets. Track metrics over time, debug regressions instantly, and ensure consistent performance from development to deployment.

Build your own Eval Framework

Use our flexible framework to define custom metrics, test logic, and success criteria tailored to your LLM, agent, or product requirements.

Build your own Eval Framework

Use our flexible framework to define custom metrics, test logic, and success criteria tailored to your LLM, agent, or product requirements.

Build your own Eval Framework

Use our flexible framework to define custom metrics, test logic, and success criteria tailored to your LLM, agent, or product requirements.

Custom Evaluators Tech & Non-Tech

The only platform where both developers and non-technical users can build custom evals. Define what “good” looks like from retrieval to reasoning, with code or no-code.

Custom Evaluators Tech & Non-Tech

The only platform where both developers and non-technical users can build custom evals. Define what “good” looks like from retrieval to reasoning, with code or no-code.

Custom Evaluators Tech & Non-Tech

The only platform where both developers and non-technical users can build custom evals. Define what “good” looks like from retrieval to reasoning, with code or no-code.

Safeguard your AI systems

Safeguard your AI with automated safety checks and customizable guardrails. Ensure reliable, compliant, and controlled model behavior at all times.

Safeguard your AI systems

Safeguard your AI with automated safety checks and customizable guardrails. Ensure reliable, compliant, and controlled model behavior at all times.

Safeguard your AI systems

Safeguard your AI with automated safety checks and customizable guardrails. Ensure reliable, compliant, and controlled model behavior at all times.

Evaluation Wizard - Built for tech and non-technical Users

Evaluation Wizard - Built for tech and non-technical Users

Enable anyone on your team, product, devs, QA, domain experts to define your quality framework and run evaluations with zero code

No-code interface

to build eval logic and workflows

Test new prompts or models

before they’re pushed to production

Collaborate across teams

with shared evaluations and result views

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import langwatch

evaluation = langwatch.evaluation.init("my-experiment")

for idx, row in evaluation.loop(df.iterrows()):
response = my_agent(row["question"])

evaluation.log("sample_metric", index=idx, score=0.95)

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import langwatch

evaluation = langwatch.evaluation.init("my-experiment")

for idx, row in evaluation.loop(df.iterrows()):
response = my_agent(row["question"])

evaluation.log("sample_metric", index=idx, score=0.95)

API-based or platform native -keep control where required

API-based or platform native -keep control where required

CI/CD Evaluation Pipelines

Run evaluations continuously as part of your deployment stack. Keep your own infrastructure and workflows — no changes required.

Evaluate Structured Outputs and Tool Calls

Support complex evaluation formats, from JSON and function calling to multi-tool agents. Understand how your models behave in real-world applications

Air-Gapped and Self-Hostable

Run everything locally or in your private cloud. No lock-in, no external dependency full control over your data, models, and eval flows.

Ship agents with confidence, not crossed fingers

Get up and running with LangWatch in as little as 5 minutes.

Ship agents with confidence, not crossed fingers

Get up and running with LangWatch in as little as 5 minutes.

Ship agents with confidence, not crossed fingers

Get up and running with LangWatch in as little as 5 minutes.