Respan

How does Respan help debug agent failures and improve workflows?

Respan gives you traces, logs, evals, and workflow-level visibility so you can see what your agent did, where it failed, which prompt or tool call caused the issue, and what changed between versions. Instead of debugging from scattered logs or user reports, you can inspect failures directly and turn production behavior into a tighter improvement loop.

Will tracing with Respan affect my product’s performance?

Respan is built to add observability without becoming a bottleneck in your request path. In most setups, tracing is lightweight and designed for production use, so teams can monitor real agent behavior without introducing meaningful latency for end users.

How do I integrate Respan, what frameworks, SDKs, and providers does it support?

Respan is designed to fit into existing AI stacks without forcing a rebuild. It supports popular frameworks, SDKs, and providers including OpenAI, Anthropic, LangChain, LlamaIndex, Vercel AI SDK, OpenAI Agents SDK, and many others, so you can start tracing, evaluating, and routing model traffic inside the workflows you already run.

Should I use the Respan gateway and prompt management?

Use the Respan gateway and prompt management if you want one layer to control routing, fallbacks, prompt versions, experiments, and production traffic without constantly redeploying application code. If you only need observability and evals, you can start there first and adopt gateway or prompt management later as your stack gets more complex.

What do evals actually do in Respan?

Evals in Respan help you measure the quality of model and agent outputs instead of relying on guesswork. You can score traces, compare prompt or model changes, catch regressions, and understand whether a change actually improved reliability, correctness, or task performance. Respan also supports online evals, so you can monitor production behavior in real time and detect issues as they happen.

How is Respan different from Langfuse, LangSmith, or Braintrust?

Respan is built for teams that care about scalability, not just visibility. It combines observability, evals, gateway controls, and prompt management in one platform so you can monitor agent behavior, catch issues proactively, and keep improving systems as traffic, complexity, and team size grow. The focus is not only on inspecting traces, but on giving teams a more self-driving and scalable workflow for operating production AI.