Langfuse alternative
Langfuse shows you what happened. RunGuard stops the next call before it fires.
If your team is already using Langfuse for tracing and wants to add a runtime circuit breaker — one that halts tool calls when a loop pattern is detected, before the next LLM request goes out — RunGuard fills that gap without replacing your observability stack.
Why teams look for a Langfuse alternative
- Post-hoc tracing is not prevention. Langfuse's SDK wraps your LLM calls and logs every event to its backend. Those callbacks fire after the call returns — they cannot halt the next invocation before it executes. If your agent loops 20 times before you notice, Langfuse gives you a beautiful trace of all 20 calls. RunGuard trips at call 3.
- Self-hosted Langfuse has real maintenance overhead. The self-hosted stack requires Postgres, Redis (for some deployments), Docker Compose or Kubernetes, and ongoing upgrades. If you are a 1–3 person AI startup, that infra overhead is disproportionate to what you need right now. RunGuard is a one-line SDK install with no infra.
- Async eval pipelines cannot fire fast enough to stop a live loop. Langfuse's evaluation features — scores, dataset runs, online sampling — are asynchronous by design. They are meant for quality improvement after runs complete. There is no synchronous pre-call hook that can throw an exception and halt the agent mid-run.
Feature comparison
| Langfuse | RunGuard | |
|---|---|---|
| Real-time loop detection (pre-call) | ✗ | ✓ |
| Context-window overrun guard | ✗ | ✓ |
| Per-run budget cap (USD) | ✗ | ✓ |
| Post-run trace and replay | ✓ | ✗ |
| Prompt management and versioning | ✓ | ✗ |
| Evaluation datasets and scoring | ✓ | ✗ |
| Open-source / self-hostable | ✓ | ✗ |
| Starting price | Free (OSS) / $59/mo cloud | $0 trial / $19/mo Solo |
How RunGuard is different
Langfuse and RunGuard solve adjacent but non-overlapping problems. Langfuse's SDK observes your calls — it fires callbacks after each LLM response returns, records the trace, and lets you query, evaluate, and replay runs after they finish. RunGuard's guard() wrapper sits one level above your LLM client and makes a synchronous pre-call decision: has this call signature appeared three or more times in the current run window? If yes, it throws LoopDetectedError before the HTTP request goes out. The two tools compose cleanly:
// TypeScript — compose RunGuard + Langfuse tracing import { guard } from "@runguard/sdk" import { observe } from "langfuse" const safeAgent = guard(observe(callYourLLM), { repeats: 3, maxUsd: 5, windowMs: 60_000 }) // RunGuard halts before the call. Langfuse traces what RunGuard allows.
When Langfuse is still the right choice
If your team's primary need is post-run observability, prompt versioning, or evaluation datasets — Langfuse is the better tool. Its trace replay, annotation queues, and dataset management features are mature and well-supported. If you are building LLM evaluation pipelines, integrating human-in-the-loop review, or need deep prompt-management workflows, Langfuse is purpose-built for that. RunGuard is not an observability platform and will not replace those features. The ideal setup for teams shipping autonomous agents is Langfuse for observability and RunGuard for prevention — they layer cleanly.
FAQ
- Can I use RunGuard alongside Langfuse?
- Yes, that is the recommended setup. Wrap your LLM function with
guard()first, then wrap that with Langfuse's tracing. RunGuard prevents loops; Langfuse traces every call that RunGuard allows through. The two SDKs are completely independent. - Does RunGuard replace Langfuse's evaluation features?
- No. RunGuard has no eval dataset management, no annotation queues, no prompt versioning. It does one thing: trips a circuit breaker when a loop pattern, context overrun, or budget threshold is detected before the next call fires. If you need eval pipelines, keep Langfuse.
- What loop patterns does RunGuard detect?
- RunGuard hashes the tool name, arguments, and call context into a signature and counts repetitions in a rolling window. When the same signature repeats
Ntimes (default: 3) withinwindowMsmilliseconds, it throwsLoopDetectedError. It also detects context-window drift (token count approaching model limit) and per-run USD overruns.