langwatch
@langwatch Organization
Public Skills
scenarios
by langwatch
Test your AI agent with simulation-based scenarios. Covers writing scenario test code (Scenario SDK), creating platform scenarios via the langwatch CLI against a connected agent, reading the run parameters that agent declares so the scenarios and comparison runs turn its real levers, and red teaming for security vulnerabilities. Auto-detects whether to use code or platform approach based on context.
code-changes
by langwatch
Change the user's own program, on their machine or through GitHub. Use when a request needs a change to the user's code (instrument tracing, wire the SDK, fix the agent behind a failing scenario, add a run parameter to a connected agent, version a hardcoded prompt) and not when the platform alone can do it (create a scenario, an evaluation, a prompt version, read traces).
prompt-optimization
by langwatch
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a prompt, when they arrive from the workbench's "Optimize this prompt" menu item, or when they want their bot to answer better. Bootstraps a missing dataset or evaluator first.
tracing
by langwatch
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.
evaluations
by langwatch
Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.
connect-agent
by langwatch
Connect the codebase's AI agent to LangWatch agent simulations, so test suites run against the real agent process. Adds a small connect function beside the service startup that calls the agent already in the codebase, which opens an outbound connection and registers the agent with its environment and its run parameters, confirms the agent is Online, and runs the first test suite. Falls back to an HTTP registration when the agent cannot import the SDK. Use when the user wants platform scenarios to test their real agent.
prompts
by langwatch
Version and manage your agent's prompts with LangWatch Prompts CLI. Use for both onboarding (set up prompt versioning for an entire codebase) and targeted operations (version a specific prompt, create a new prompt version). Supports Python and TypeScript.
github
by langwatch
Open a real pull request. Clone a repo, branch, commit, push, and open a PR authored by the LangWatch app on behalf of the requesting user. Use when the user asks to open a PR, fix something in a repo and submit it, send a patch, raise a pull request, or otherwise land a code change on GitHub.
agent-best-practices
by langwatch
Expert AI engineering consultant for your agent development practices. Audits your codebase, traces, evaluations, and scenarios against best practices, then guides you to close the gaps, starting from low-hanging fruit and going deeper. Use when you want to level up your agent's engineering quality.
provider-cost-comparison
by langwatch
Prices your real LangWatch usage mix against other model providers. Exports your actual token mix per model, including the cache read and write split, fetches current price cards, and reprices the same month of usage under each candidate, with the cache sensitivity stated. Use when someone asks whether a cheaper provider or model would actually save money on your workload.
context-sweet-spot
by langwatch
Investigates the context economics of your own coding-agent sessions in LangWatch. Reads real sessions to find where carrying a fat context stops paying for itself, measured in cache rebuilds, compactions and cost per turn, and delivers a report with the context size your sessions should stay under, with example sessions behind every claim. Use when coding-agent sessions feel expensive or degrade as they grow.
experiments
by langwatch
Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.
drive-the-ui
by langwatch
Drive the page the user has open through live UI actions. List the actions a page accepts, call them with typed payloads, and read the live state including unsaved edits. Use when the user is looking at a page you can operate, such as the evaluations workbench, and a change should happen in front of them rather than behind their back.
setup-lw
by langwatch
Set up and troubleshoot the LangWatch CLI, covering login (cloud and self-hosted), endpoint configuration, project selection, and connection problems. Use when the CLI isn't authenticated, can't reach LangWatch, or talks to the wrong project.
agent-improve
by langwatch
Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that capture production signals, and experiments that settle open questions. Use when you want to know what to do next to improve your agent.
online-evaluations
by langwatch
Configure LangWatch online evaluations and guardrails for production traffic. Use when the user wants to score live traces or threads, monitor production quality, sample incoming traffic, or synchronously block unsafe requests and responses. Do not use for batch experiments.
level-up
by langwatch
Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.
lwql-charts
by langwatch
Author a saved analytics chart from a plain question and place it on a dashboard. Discovers the LangWatchQL analytics schema, writes and test-runs the SQL, saves it as a chart with a Vega-Lite specification, and places it where the team already looks. Use when asked to build, save, run, or dashboard a metric or chart.
debug-instrumentation
by langwatch
Debug and improve your LangWatch traces. Inspects production traces for missing input/output, disconnected spans, unlabeled traces, and missing metadata. Use when traces look broken or incomplete.
agent-performance
by langwatch
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you want to truly understand what your agent is doing in production.
eval-triage
by langwatch
Investigate failing experiments and evaluations with LangWatch. Triage a failing experiment run to the exact rows and evaluator scores that regressed, then to a root cause. Use when an experiment fails, scores drop, or evaluations regress.
debug-with-langwatch
by langwatch
Root-cause production errors and misbehaving agent runs with LangWatch. Finds errored traces, inspects spans, checks monitor and evaluator scores, then narrows to a root cause. Use when something is failing or misbehaving in production (errors, bad answers, latency spikes).
test-compliance
by langwatch
Test that your AI agent stays observational and doesn't give prescriptive advice in regulated domains (healthcare, finance, legal). Creates scenario tests for boundary enforcement and red team tests for adversarial probing. Use when your agent advises but must not prescribe.
generate-rag-dataset
by langwatch
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.