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.
Evaluate multimodal AI agents that process images, audio, PDFs, or other files. Sets up evaluations using LangWatch's LLM-as-judge with image inputs, Scenario's multimodal testing, and document parsing evaluation patterns. Use when your agent handles non-text inputs.
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.
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.
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.
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.
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.
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.
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.
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…
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.
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…
Test your AI agent with simulation-based scenarios. Covers writing scenario test code (Scenario SDK), creating platform scenarios via the langwatch CLI, and red teaming for security vulnerabilities. Auto-detects whether to use code or platform approach based on context.
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.
Write scenario tests that verify your CLI tool is usable by AI agents. Ensures commands work non-interactively, provide clear output, and don't hang on prompts. Use when you want to prove your CLI is agent-friendly.
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.
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.