myAI vs Langfuse

TL;DR: Langfuse is an open-source LLM engineering platform — tracing, observability, prompt management, evals, and cost analytics for LLM applications you run in production. myAI is a continuity OS for your own AI-driven development. The overlap people assume is "both track cost," but they meter different resources: Langfuse meters API tokens your app spends; myAI meters your Claude subscription's rolling window — the resource that actually constrains a Claude Code power user. If you ship an LLM app, run Langfuse. It won't do anything about your agents' amnesia.

What Langfuse does well

Langfuse is one of the most credible self-hostable tools in the LLM ecosystem:

• Tracing done right. Nested traces of LLM calls, tool calls, and retrievals with latency and token cost per span — the debugging surface every production LLM app eventually needs.
• Prompt management — versioned prompts, deployment labels, rollbacks, decoupled from code deploys.
• Evals — LLM-as-judge pipelines, human annotation queues, and dataset-based scoring wired to real traffic.
• Cost analytics — API token usage per model, per user, per feature; the numbers a team needs to keep an API bill under control.
• Genuine open source with first-class self-hosting. The self-hosted deployment is a supported path, not an afterthought — a value myAI shares.

When you should choose Langfuse

• You run an LLM application in production and need to debug traces, watch latency/cost, and evaluate quality over time.
• Your team iterates on prompts and needs versioning and controlled rollout.
• You pay per API token and need attribution of that spend.

myAI does none of this. It has no tracing, no prompt registry, no eval pipelines. If those are your needs, there is no comparison to make — use Langfuse.

What myAI adds that Langfuse doesn't attempt

Langfuse observes an application from the outside. myAI runs the operator's loop — the layer where the question isn't "how did my app behave?" but "do my agents remember me, and is my queue moving?"

• Continuity, the core product. State, handoff, and a distilled memory corpus persist across sessions; any MCP-capable agent boots already knowing your context. Observability tells you what happened; continuity makes the next session act on it.
• The subscription budget model — the actual gap. Every observability tool meters API tokens. But Claude Code Pro/Team users don't pay per token — they pay a subscription with a rolling usage window, and that window is the scarce resource. myAI meters real output-token burn across all your sessions and repos against that window, warns as it fills, and checkpoints sessions (write the handoff now) so hitting the ceiling never strands work. As far as we know, nothing else is built for subscription-billed agent work.
• Turning the leftover window into output. The off-hours runner spends the subscription capacity you'd otherwise waste while asleep — queued tasks get built at $0 marginal cost and land in a review lane. A cost dashboard helps you watch spend; myAI's loop converts spare capacity into shipped work.
• Fleet management — one gateway/dashboard across all your repos: directory, morning console, health checks.
• Memory with provenance. The git-versioned brain records what your agents learned, diffable and revertible — closer to version control than to telemetry.

Where Langfuse is genuinely ahead

• Everything observability. Traces, spans, evals, prompt ops — myAI has none of it and doesn't plan to.
• Team/production maturity — RBAC, wide SDK/framework coverage, a large deployed base.
• API-token cost attribution for deployed apps is simply a different (and well-served) problem from myAI's window budgeting.

Using both

No conflict anywhere: Langfuse watches the app you ship; myAI runs the workflow that ships it. Your product's LLM traffic flows through Langfuse tracing; your repos, sessions, task queue, and subscription budget flow through myAI. A myAI-managed repo can be an app instrumented with Langfuse — different layers, both self-hosted on your own boxes.

Side by side

Bottom line: Langfuse answers "what is my LLM app doing, and what does it cost per token?" myAI answers "do my agents remember me, is my queue moving overnight, and how much of my subscription window is left?" Both are self-hosted and open — and if you're a developer who ships LLM apps and lives in Claude Code, you'll likely end up running both.

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