LangWatch vs Converra: Testing Agents and Verifying Fixes
Converra fixes production agent behavior: diagnose the failing step, generate a bounded prompt or configuration change, test it against the current agent, ship what you approve, and return a production verdict. LangWatch also helps teams improve agents, with tracing, multi-turn scenario testing, automatic prompt optimization, and production evaluations.
The useful comparison is how each workflow connects a production failure to a tested change and then to evidence after release. Calling LangWatch just a dashboard would miss its Scenario testing and DSPy optimization capabilities. This page uses the same dimensions as the agent optimization hub.
The short version
Choose LangWatch when you want an open-source testing and tracing platform with scenarios and optimizer control. Choose Converra when you want a managed behavior-fix loop that carries each approved change through production verification.
LangWatch vs Converra, side by side
Each row answers the same question for both products. LangWatch statements come from its own documentation, reviewed on the date shown above; sources are listed at the end.
- Dimension
- Primary job
- LangWatch
- Open-source platform for tracing, evaluating, testing and optimizing agents and LLM applications
- Converra
- Runs the improvement loop for production agents: diagnose, fix, simulate, ship with approval, verify
- Dimension
- Production evidence
- LangWatch
- SDK and OpenTelemetry traces, user feedback, datasets, and live trace or conversation evaluations
- Converra
- Imports traces through LangSmith and Langfuse connectors, or receives conversations through its SDK and traces API, which accepts OpenTelemetry-compatible data; it is not a tracing tool
- Dimension
- Failure diagnosis
- LangWatch
- Trace inspection, topic clustering and online evaluators surface failures; teams define the criteria their scenarios should enforce
- Converra
- Step-level diagnosis that labels and counts recurring failure patterns across conversations
- Dimension
- Fix generation
- LangWatch
- Langy rewrites a prompt and compares it on the same dataset; DSPy can optimize an agent using full multi-turn scenarios as its metric
- Converra
- Generates bounded prompt or configuration variants aimed at a diagnosed pattern
- Dimension
- Testing before release
- LangWatch
- Scenario user simulation, judge criteria, CI runs and comparison runs that send the same scenarios to multiple targets
- Converra
- Head-to-head multi-turn simulation against the current agent on the same personas, plus regression scenarios
- Dimension
- Deployment and approval
- LangWatch
- Langy asks whether to publish a winning prompt or keep it as a draft; Prompts CLI and GitHub Actions support prompt versioning and sync
- Converra
- Review-first by default: a GitHub pull request or a direct update a person applies; optional auto-deploy requires a passing regression test
- Dimension
- Verdict after release
- LangWatch
- Online monitors score real traces or idle threads and feed analytics and automations; the cited workflow does not specify Converra's per-fix verified, not fixed or confounded decision
- Converra
- Measures the targeted failure on live traffic and marks the fix verified, not fixed, or confounded
- Dimension
- Hosting
- LangWatch
- Managed cloud or self-hosted deployment; Enterprise licensing adds identity, access-control and support features
- Converra
- Managed cloud service; the Enterprise plan lists an on-prem or VPC option
Both products can test and improve a prompt
Langy's automatic prompt optimization scores the baseline, duplicates and rewrites the prompt, and runs the candidate on the same dataset rows with the same evaluator. Its DSPy guide goes further: the metric runs a full agent conversation, so the optimizer can change instructions and tool descriptions based on scenario outcomes. Converra also generates and compares candidate fixes. Automatic prompt rewriting is not an exclusive Converra capability.
LangWatch comparison runs send the same scenarios to multiple targets and report pass rate, cost and latency. Converra's winner selection uses paired, persona-level head-to-head lift and retains the baseline when no challenger has positive lift. These are related testing approaches, not a claim that one product has simulation and the other does not.
A refund fix needs two kinds of evidence
Suppose a support agent promises refunds outside policy. Before release, test eligible returns, expired returns, ambiguous purchase dates and escalation cases against both versions. LangWatch can run that suite and optimize toward its criteria. Converra generates a bounded behavioral fix and tests it against the current agent with regression coverage.
After approval, check whether the targeted refund failure recurs on real conversations. LangWatch's online monitors provide live evaluator results. Converra connects the shipped change to a production verdict. Its confounded outcome covers overlapping edits to the same prompt; it does not automatically rule out model changes or a shift in customer traffic. The production verification guide explains the evidence limits.
When to choose LangWatch
You want to own scenarios, evaluators and DSPy optimization code, with a platform that records their results.
You need tracing, prompt management and production evaluations alongside agent tests.
You need a self-hosted platform and are prepared to operate its infrastructure.
When to choose Converra
You have recurring production behavior failures and want bounded fixes generated from their diagnosis.
You want the current agent and candidate compared head-to-head before you approve a change.
You want the approved fix tracked afterward with a verified, not fixed or confounded production verdict.
Does Converra replace LangWatch?
They overlap on prompt improvement and agent simulation. Converra is not a replacement for every LangWatch tracing, evaluator or self-hosting feature.
Do not assume a native LangWatch connector: Converra's documented native trace connectors are LangSmith and Langfuse. For another stack, use the Converra SDK integration guides to send supported conversation data. Check capture coverage before deciding the systems work together.
Frequently asked questions
Does LangWatch automatically optimize prompts?
Yes. Langy scores and rewrites a prompt against an evaluator and dataset. LangWatch also documents DSPy optimization with multi-turn scenarios as the metric. The team decides whether to publish the winning prompt.
Does LangWatch support agent simulation?
Yes. Scenario supports simulated users and judges, and comparison runs send the same scenarios to multiple targets. Converra also uses multi-turn simulation, with paired lift to select a candidate.
Is passing a scenario suite proof the production fix worked?
No. A suite shows how a candidate behaved in those test conditions. Live evaluations can detect recurrences; a production verdict must also identify the shipped change, targeted failure and relevant evidence limits.
Can I call Converra from Claude Code, Codex or Cursor?
Yes. The remote MCP server exposes authorized diagnosis, optimization, simulation and deployment-impact tools. Start with the coding-agent setup guide and approve any operation that changes state or incurs inference cost.
Written by
Oren CohenFounder of Converra. Previously founded Buildup (acquired by Stanley Black & Decker) and, as VP Product Growth at Totango, owned AI end-to-end from design through production.
Related reading
Take a production failure through the fix loop
Converra fixes the behavior, not the code. Diagnose the failing step, compare a bounded fix with the current agent, approve what goes live, and get a production verdict: verified, not fixed or confounded.