LangSmith vs Converra: What Each Does for Production Agents

Each row answers the same question for both products. LangSmith statements come from its own documentation, reviewed on the date shown above; sources are listed at the end.

Both start from production traces and both move past dashboards. LangSmith Engine watches a tracing project, groups failures into named issues, diagnoses root causes against your code, and proposes a fix as a pull request. Converra also diagnoses recurring failures from production conversations and proposes fixes. If you already use Engine, the first half of the loop will look familiar.

What the fix changes. Engine's documentation describes code changes in a connected repository. Converra generates prompt and configuration variants aimed at a diagnosed behavior, such as routing, grounding, or a policy the agent keeps breaking.

How the fix is tested. Engine's fix validation, in private beta, replays the issue's failing traces against a preview deployment built from the pull request. Converra runs the candidate and the current agent head-to-head in multi-turn simulation with the same personas, counts only paired comparisons, and checks regression scenarios the agent already handles.

What happens after release. Engine reopens an issue if the failure recurs in a later trace. Converra counts the targeted failure on live traffic and returns a verdict: verified when at least 15 diagnosed conversations show zero recurrences, not fixed when it recurs at least as often, and confounded when another change to the same prompt overlapped. The guide to verifying a fix after deployment explains why that distinction matters.

You want one platform for tracing, evaluation datasets, experiments, and prompt versioning, especially if you build with LangChain or LangGraph.

Your recurring failures are mostly code problems, and you want proposed pull requests generated from production issues.

You need self-hosting or bring-your-own-cloud deployment for your observability data.

Your recurring failures are behavioral, such as wrong routing, unsupported claims, or broken policies, and fixable in prompts or configuration.

You want every candidate fix tested head-to-head against the current agent in multi-turn simulation before release, with regression scenarios.

You want each shipped fix to come back with a production verdict: verified, not fixed, or confounded.

Your agent is conversational. Converra imports multi-turn LangSmith traces with a system prompt and skips single-turn tasks such as summarization or classification.

No. Converra complements LangSmith. It connects with a LangSmith API key, imports multi-turn traces and feedback from the projects you choose, and does not replace tracing, datasets, experiments, or prompt versioning.

The overlap is with Engine. Teams using both can let Engine handle code-level issues and Converra handle prompt-level behavior, with Converra's verdict as the post-release check for the changes it ships.