Foglamp
Summary
Your agent ships clean, then three weeks later costs have doubled, answers are wrong, and customers are already posting about it — and you have no trace of when or why it broke.
Foglamp is an observability layer built for production AI agents: two lines of SDK integration wrap every `generateText` and `streamText` call and surface cost, latency, distributed traces, per-agent spend, and output quality in one place. The instrumentation is designed specifically around the Vercel AI SDK, so teams already on that stack see immediate coverage without rethinking their pipeline. Evals and alerts let you catch output regressions before users file support tickets. The ceiling appears when your stack moves outside Vercel AI SDK conventions — the docs describe no native integrations for other frameworks, and teams on LangChain or custom agent loops will need to assess how much of the trace fidelity carries over.
Bottom line: Foglamp earns its keep the week a cost regression would otherwise go undetected in a Vercel AI SDK production deployment — but teams running heterogeneous agent stacks across multiple frameworks will find the observability coverage uneven enough to reach for a more framework-agnostic alternative.
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Pros
Sign in to edit- Two-line SDK instrumentation wraps every Vercel AI SDK call automatically, so you get cost and trace coverage without rewriting your agent logic or adding per-call boilerplate.
- Per-agent spend breakdown attributes token costs to individual agents or orchestrator steps, which means a cost spike is diagnosable in the dashboard rather than requiring a manual log scrape across your pipeline.
- Distributed traces across the full call flow let you see exactly which step added latency, so performance regressions don't require you to reproduce the issue locally.
- Output quality evals with configurable alerts catch answer regressions before users encounter them — the failure mode Foglamp exists to prevent is a customer complaint thread, not a monitoring page.
- API access is available, so teams that want to pull observability data into existing dashboards or incident workflows are not locked into the Foglamp UI.
Cons
Sign in to edit- The SDK integration is documented specifically around `generateText` and `streamText` in the Vercel AI SDK — teams running LangChain, LlamaIndex, or custom agent frameworks get no native wrapping, and at that point they are either writing manual instrumentation or evaluating a framework-agnostic alternative like Langfuse or Helicone.
- All telemetry routes through Foglamp's cloud infrastructure; self-hosting is not offered, which means any team with strict data-residency or compliance requirements is blocked at the architecture stage before the first line of instrumentation is written.
- Evals and alert thresholds require upfront configuration to return signal — teams that ship without defining quality criteria first get cost and latency data but no regression detection, which is the half of the value proposition that justifies the instrumentation cost.
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About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-06-19T21:23:28.429Z
Best For
Who it's for
- Developers using Vercel AI SDK
- Teams shipping production AI agents
- Monitoring cost and quality regressions
What it does well
- Monitor LLM call costs and token usage
- Track latency and distributed traces for agent flows
- Run evals and set alerts on output quality
- View per-agent spend and performance
Integrations
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Frequently Asked Questions
- Is Foglamp free?
- Foglamp is a paid tool. No permanent free tier is offered.
- Is Foglamp open source?
- Yes. Foglamp is open source.
- Does Foglamp have an API?
- Yes. Foglamp exposes a developer API. See the official documentation at https://foglamp.dev for details.
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Curated lists that include this category
Week one your agent looks fine. By week three, token costs have quietly doubled and the model is confidently inventing refund policies you retired in March — and you find out from a customer tweet, not a dashboard. Foglamp exists to close that gap. The vendor describes it as ‘the missing observability layer for AI agents’: instrument once with the Foglamp SDK, and every LLM call emits cost data, latency traces, per-agent spend breakdowns, and eval results without additional configuration. The core workflow is two lines of import and initialization; from there, every `generateText` and `streamText` call is wrapped automatically.
The standout differentiator is the per-agent spend view paired with distributed traces across a full call flow. Rather than aggregating everything into a single cost line, Foglamp surfaces which agent or orchestrator step is responsible for the spend — so when costs spike, the query to find the culprit is already answered. Alerts on output quality mean you can define acceptable eval thresholds and get notified when answers degrade, rather than waiting for a support queue to surface the signal.
Foglamp fits teams that are already shipping on the Vercel AI SDK and want production visibility without standing up a separate monitoring stack. The integration story is explicit and narrow: the page calls out `generateText` and `streamText` specifically, and no other framework integrations are described in the available documentation. Teams running agents built on other frameworks, or mixed stacks, face an open question about trace fidelity that the current docs do not answer. Self-hosting is not available, so all telemetry routes through Foglamp’s cloud — a constraint that matters for teams with data-residency requirements. The project has a GitHub presence referenced on the product page, though no license details or repository link appear in the available documentation.