# Handit vs Converra: Production Agent Fixes Source: https://converra.ai/compare/handit Published: 2026-10-18. Updated: 2026-10-18. Sources reviewed: 2026-10-18. Publisher: Converra (https://converra.ai), the autonomous improvement loop for production AI agents. Handit describes an open-source workflow that traces an AI agent, detects failures, generates fixes, tests them and opens a pull request. Converra fixes production agent behavior: diagnose the failing step, generate a bounded prompt or configuration change, compare it with the current agent, ship what you approve, and return a production verdict. Both products begin with production failures and aim to make the next engineering decision concrete. The useful distinction is whether a proposed fix, a pull request, a pre-release comparison and a live result remain separate evidence. This page uses the same dimensions as the [agent optimization hub](https://converra.ai/ai-agent-optimization). > Choose Handit when you want an open-source, GitHub-connected workflow that detects agent failures and creates tested pull requests. Choose Converra when you want a managed behavior-fix loop that compares a bounded candidate before approval and follows the approved change to a production verdict. ## Handit vs Converra, side by side Each row answers the same question for both products. Handit statements come from its own documentation, reviewed on the date shown above; sources are listed at the end. *Handit compared with Converra* | Dimension | Handit | Converra | | --- | --- | --- | | Primary job | Open-source production agent tracing, failure detection and GitHub pull-request generation | Runs the improvement loop for production agents: diagnose, fix, simulate, ship with approval, verify | | Production evidence | Instrumented production requests, traces, quality evaluations and connected repository context | 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 | | Failure diagnosis | Automatic issue detection and root-cause-oriented findings for agent failures | Step-level diagnosis that labels and counts recurring failure patterns across conversations | | Fix generation | Prompt, configuration, code and model-parameter recommendations that can become pull requests | Generates bounded prompt or configuration variants aimed at a diagnosed pattern | | Testing before release | Handit says it tests generated fixes against failure cases; confirm a proposed pull request's coverage before relying on it | Head-to-head multi-turn simulation against the current agent on the same personas, plus regression scenarios | | Deployment and approval | GitHub pull requests keep review and release control with the engineering team | Review-first by default: a GitHub pull request or a direct update a person applies; optional auto-deploy requires a passing regression test | | Verdict after release | Handit describes tested fixes and performance data; its public repository does not specify Converra's per-fix verified, not fixed or confounded decision | Measures the targeted failure on live traffic and marks the fix verified, not fixed, or confounded | | Hosting | Open-source repository with hosted dashboard, SDKs and GitHub integration | Managed cloud service; the Enterprise plan lists an on-prem or VPC option | ## Both products start with a production failure Handit's public repository describes tracing AI applications, detecting issues such as hallucinations, schema failures, privacy leaks and timeouts, then connecting a finding to a proposed fix. Its setup links an SDK, evaluation models and a GitHub repository. That is a concrete production-improvement workflow, not passive logging. Converra also begins with production behavior, but it narrows the next step to a diagnosed failure pattern and a bounded prompt or configuration candidate. A broken service, code path or tool integration still needs an engineering repair. Neither a trace finding nor a recommendation is evidence that a behavior fix worked. ## A tested pull request is still a proposed change Handit says it can generate fixes, test them against production failure cases and open a GitHub pull request. That gives an engineering team a reviewable artifact in its normal workflow. A pull request is not itself a released change, and its public description does not make a merged change a result on future production traffic. Converra compares a bounded behavior change with the current agent before approval, with regression scenarios for behavior the baseline already handles. The team still approves what goes live. Treat a passing candidate test, an opened pull request and an approved production change as separate states so a strong suggestion is not presented as a completed fix. ## Live improvement needs an attribution boundary A support agent may begin inventing refund eligibility after a model change. Handit can surface the recurring pattern, propose a prompt or code change and send the team a pull request. Before release, review the change and test eligible refunds, expired returns, ambiguous purchase dates and escalation cases. After approval, measure the same failure on comparable production traffic. Converra records a verified, not fixed or confounded production verdict when its evidence rules support one. Confounded does not prove the fix failed: overlapping edits to the same prompt can prevent attribution, and model changes or traffic-mix shifts are not automatically excluded. This is a workflow example, not customer performance evidence. ## When to choose Handit You want an open-source, GitHub-connected system to trace production agent failures and create reviewable fixes. Your engineering team wants recommendations that can include prompts, configuration, code or model settings. You want to investigate failures through an instrumented SDK and keep the release decision in your repository workflow. ## When to choose Converra A recurring production behavior failure needs a bounded prompt or configuration candidate, not only a proposed code change. You want the current agent and candidate compared head-to-head before approving a change. You want the approved behavioral fix tracked afterward with a verified, not fixed or confounded production verdict. ## Does Converra replace Handit? They overlap on using production evidence to improve an agent. Converra does not replace every code-analysis, SDK-instrumentation or GitHub pull-request workflow in Handit. Do not assume a native Handit connector. Converra's documented native trace connectors are LangSmith and Langfuse. For another stack, use the [Converra SDK integration guides](https://converra.ai/docs/guide/integrations/) to send supported conversation data and verify capture coverage before relying on the combined workflow. ## Sources - Handit public repository and workflow overview (Handit): https://github.com/Handit-AI/handit.ai - Handit quickstart and setup requirements (Handit): https://github.com/Handit-AI/handit.ai#quick-start - Handit JavaScript and TypeScript SDK package (npm): https://www.npmjs.com/package/@handit.ai/handit-ai - Handit Python SDK package (PyPI): https://pypi.org/project/handit-ai/ ## Frequently asked questions ### Does Handit create fixes for AI agents? Handit's public repository says it detects failures, generates and tests fixes, then ships them as pull requests. Review the proposed change and its test coverage before treating it as a deployed result. ### Does a tested pull request prove an agent is fixed? No. A pull request is a reviewable proposed change. You still need pre-release testing, an approved release, comparable production evidence and clear attribution limits before calling the behavior fixed. ### What does Converra add after a production finding? Converra turns a diagnosed behavior failure into a bounded candidate, compares it with the current agent in simulation, keeps regression coverage, and records verified, not fixed or confounded evidence after an approved deployment. ### Can I use 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. ## Related reading - [Add Converra to your coding agent](https://converra.ai/docs/mcp/coding-agents): Connect Claude Code, Codex or Cursor to the production fix workflow. - [Forecast versus measured result](https://converra.ai/blog/expected-impact-vs-production-verdict): Keep candidate, release and production evidence separate. - [Production agent-fix tools](https://converra.ai/blog/best-tools-to-fix-production-ai-agents-2026): See where trace analysis, proposed changes and production verification fit together. ## 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. See the coding-agent workflow: https://converra.ai/docs/mcp/coding-agents Markdown edition of https://converra.ai/compare/handit. The HTML page is canonical; this file is for agents and answer engines.