langwatch vs maka
LLM operations and agent workflow workspaces compared
langwatch helps teams test and monitor LLM applications and agents, with observability, prompt management, gateway, and governance workflows. maka focuses on running and inspecting agent tasks, recording execution events locally and supporting recovery of interrupted work.
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.
maka: Run Agents in a Workspace with a Complete Activity Log
Apache Maka is a TypeScript agent workspace for running tasks through a shared runtime and keeping a complete, append-only record of each run. Its Electron desktop app supports macOS, with Windows and Linux previews, and can connect to cloud or local models you provide.
| langwatch | maka | |
|---|---|---|
| Language | TypeScript | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 4.9k | 5.8k |
| Forks | 405 | 557 |
| Last analyzed | Oct 3, 2026 | Oct 19, 2026 |
Key differences
- langwatch covers production observability, simulation testing, prompt management, AI gateway, and governance; maka centers on agent execution records and evaluation tooling.
- langwatch offers cloud and self-hosted options; maka keeps workspace state and session records on the user's machine and connects to user-provided models or gateways.
- langwatch includes provider routing, virtual keys, and budgets; maka records model messages, tool calls, permission decisions, and termination in an append-only event log.
- Both projects use TypeScript and Apache-2.0 licensing, while langwatch notes that enterprise modules require a commercial license for production use.
- maka is an Apache Incubator project and says it has not made an Apache release; langwatch directs production self-hosting users to separate deployment guidance.
Choose langwatch if you…
- need to test agent behavior and monitor LLM applications in production.
- want prompt management alongside observability, simulation testing, or evaluation.
- need shared model routing, usage budgets, or organizational AI governance workflows.
Choose maka if you…
- want to inspect agent runs through a local, append-only execution history.
- need Desktop, TUI, or CLI entry points for running agent tasks.
- want to evaluate agent harnesses with experiments and per-task results, or resume eligible interrupted turns.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.