AgentWach vs Langfuse
Langfuse helps you trace and evaluate. AgentWach adds budget enforcement.
Langfuse is a strong choice for LLM observability, prompt management, and evaluation — especially if you want to self-host. AgentWach is positioned one step earlier in the incident chain: it enforces budgets and kills loops before they show up in your trace viewer.
Where Langfuse shines
- Open-source core, self-hostable on your own infra.
- Rich session & trace UI with span-level cost breakdowns.
- Prompt management, versioning, and A/B evaluation.
- Healthy ecosystem of SDKs and framework integrations.
Where AgentWach wins
- Real-time budget caps with stop=true enforcement — not retrospective dashboards.
- Loop detection on tool-call patterns, not just LLM spans.
- Cross-provider budget: one cap covers OpenAI + Anthropic + Gemini + Copilot.
- GitHub Copilot premium-credit tracking out of the box.
- Prompt-injection scanner runs on every ingest.
Feature comparison
| Feature | AgentWach | Langfuse |
|---|---|---|
| Real-time token & cost tracking | Yes | Yes |
| Cross-provider rollup (OpenAI, Anthropic, Gemini, Copilot) | Yes | Per-trace only |
| GitHub Copilot premium-request credit tracking | Yes | No |
| Hard budget caps that stop agents mid-run Returns stop=true on the next ingest call. | Yes | No |
| Tool-call loop detection | Yes | No |
| Prompt-injection scanner (heuristic, 20+ rules) | Yes | No |
| Slack / Discord / PagerDuty / SMS alerts | Yes | Email/Slack only |
| Tracing & evaluation tooling | Replay + diagnose | Strong |
| Browser extension (ChatGPT / Claude / Gemini capture) | Yes | No |
| Free tier | Yes | Yes |
The bottom line
Use Langfuse when your goal is to understand and improve your LLM calls. Use AgentWach when your goal is to make sure no single agent can ever burn more than $X — and to get paged the moment one tries.