langwatch vs weave
LLM tracing, evaluation, and operations compared
langwatch and weave both help teams evaluate and understand LLM applications. langwatch covers a broader set of operational needs, including simulation testing, prompt management, gateway routing, and governance, while weave focuses on tracing and evaluations in Python workflows.
langwatch: Test and Monitor LLM Applications and AI Agents
LangWatch helps teams evaluate, test, and monitor LLM applications and AI agents in production. It combines observability, simulation testing, prompt management, an AI gateway, and governance, with cloud and self-hosted options.
weave: Trace and Evaluate Generative AI Applications
Weave helps developers trace language model inputs, outputs, and function calls, then organize and evaluate application runs. It suits teams building AI features that need debugging and repeatable comparisons across experiments and production.
| langwatch | weave | |
|---|---|---|
| Language | TypeScript | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 4.9k | 1.1k |
| Forks | 405 | 171 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- langwatch is a TypeScript platform spanning observability, simulation testing, prompt management, gateway routing, and governance; weave is a Python toolkit centered on traces and evaluations.
- langwatch supports cloud and self-hosted options, with Node.js required for self-hosting; weave requires Python 3.10 or higher and a Weights & Biases account.
- langwatch includes simulation tests for agents, including voice agents, while weave describes repeatable evaluations and trace trees for inspecting application calls.
- langwatch includes provider routing, virtual keys, budgets, coding-agent session analysis, and organizational AI governance; weave focuses on inputs, outputs, function calls, and application workflows.
- Both list Apache-2.0 licensing, but langwatch notes that enterprise modules require a commercial license for production use; weave's README says work on its engine and boards is paused while it focuses on tracing and evaluations.
Choose langwatch if you…
- need simulation testing and production observability for LLM applications or agents.
- want gateway routing, usage budgets, or governance workflows alongside evaluation.
- need to analyze coding-agent sessions and usage costs across teams or pull requests.
Choose weave if you…
- want to trace inputs, outputs, and nested function calls in Python applications.
- need structured evaluations to compare model or prompt changes.
- prefer a toolkit focused on tracing and evaluations across experimentation and production.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.