Who should use Traceloop?

It is best for developers and AI teams that need production visibility into model calls, prompts, retrieval, latency, and errors.

Does Traceloop use OpenTelemetry?

Yes. Traceloop is built around OpenTelemetry-friendly instrumentation for AI applications and LLM workflows.

Can Traceloop monitor agents?

Yes. Teams can trace multi-step agent workflows and inspect model calls, tools, and execution context.

Does Traceloop replace evaluations?

No. Observability helps teams see behavior, while evaluations define and measure whether outputs meet quality requirements.

What should teams consider?

Review trace volume, data retention, privacy, evaluation design, integration effort, and the operational cost of collecting production telemetry.

Traceloop

Name: Traceloop
Brand: Traceloop

LLM observability platform for tracing, monitoring, and debugging AI apps.

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What is Traceloop?

TL;DR

Traceloop is an LLM observability platform for tracing, monitoring, and debugging AI applications, with OpenTelemetry-friendly workflows for prompts, model calls, retrieval, agents, latency, and errors.

Trace LLM application behavior with OpenTelemetry
Monitor prompts, latency, errors, and model calls
Debug production AI workflows and agents

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About

Traceloop is an LLM observability platform for developers building AI applications. It helps teams trace, monitor, and debug model calls, prompts, agent steps, retrieval flows, and production behavior using OpenTelemetry-friendly workflows. The product is useful for SaaS builders and founders because AI apps can fail in ways that are hard to inspect. Traceloop gives developers visibility into what happened inside a model workflow, where latency appears, which prompts were used, what context was retrieved, and where errors or regressions occur. Developers can use Traceloop for chatbots, agents, RAG apps, support copilots, automation workflows, and internal AI tools. It helps move AI systems from prototype to production by making quality, cost, and behavior easier to understand. For FutureStack, Traceloop is a practical developer infrastructure tool for production AI teams. It fits the same audience as Langfuse, Helicone, and Braintrust: builders who need to monitor and improve AI products after users start using them.

Use Cases

LLM Observability

Trace model calls, prompts, retrieval steps, and agent actions to understand production AI behavior.

AI Debugging

Investigate latency, errors, unexpected outputs, and workflow regressions using detailed traces.

Agent Monitoring

Monitor multi-step agent workflows and inspect tool calls, model interactions, and execution context.

Quality Tracking

Apply evaluation and monitoring signals to detect changes in AI output quality over time.

Key Features

Trace LLM application behavior with OpenTelemetry
Monitor prompts, latency, errors, and model calls
Debug production AI workflows and agents
Works with popular LLM frameworks and providers

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