Automate code review with AI agentsthat pause before every merge.

No. Every git write, CI trigger, infra apply, and ticket transition is HITL gated by default. The crew writes the review, the diff suggestion, or the runbook draft; an engineer clicks approve before anything lands. You can lift the gate per-template once a workflow has proven itself.

Three layers. Static context attaches your architecture diagram, coding standards, and on-call playbook to specific personas on every run. The rag_retrieve tool lets BackendEngineer or PerformanceExpert pull from indexed code, ADRs, and post-mortems on demand. Cross-run memory means last week's hotspot triage is in scope for this week's follow-up.

Closer to a review and triage layer than a code-writing autopilot. Unlike Devin you keep an engineer in the loop on every commit, and unlike Cody or Codium each persona ships with a specialist prompt and a scoped toolkit. The Sweep style auto-PR is one template among many, not the only workflow.

Findings cite file and line, name a CWE or SWC number where it applies, and pull phrasing from your own ADRs and past PR comments loaded into the knowledge store. SecurityAuditor refuses to ship a finding without an attack scenario and a concrete remediation.

Any. Claude on TechLead and SmartContractExpert where reasoning depth earns the cost, GPT on the drafting personas, a local Ollama on RustPythonEngineer when source must stay on a private network. Each agent picks its own provider per template.

With a Git connector and Slack authorized, the PR review pipeline is a 4-node canvas: fetch diff, route by file glob, run RustPythonEngineer or FrontendEngineer, post comment. Most teams ship it in a single working session and see their first reviewed PR the same day.

Tool scopes restrict reads to allow-listed repos and branches, and each persona's provider is pinned per template. Route sensitive workflows to a local Ollama and the code never leaves your network. The audit log shows which model saw which file.

Every run logs every step, every tool call, every model invocation, and every approval decision. Replay any run at any time. The audit log is the change log a compliance reviewer can read end to end.

No. n8n, Zapier, and Make execute predefined trigger-action steps and cannot plan a review across an unfamiliar diff. Melaya's Security Auditor and Rust Python Engineer personas reason over the real code through read-only gitlab_public_tools access, scoped by tool allowlists, with human-in-the-loop approval on every write. Zapier and Make still win on connector breadth for simple linear automations.

Save any successful run as a template and reuse the same ten-persona crew on every repo. Melaya records full run traces with typed failure reasons and cost per accepted result, so a platform team can compare review quality across squads, tune the shared knowledge store of ADRs and coding standards, and roll out one governed pipeline.

Yes. Melaya's Device Control operates a real Android phone: an agent opens the allowed app, reads the screen, then taps and types through the reproduction steps while the full run trace records every action. Any publish or destructive step pauses for on-device approval, so QA gets a verified repro without handing credentials to a third party.

Yes. The gitlab_public_tools bundle pulls merge requests, project metadata, and file contents from GitLab, and the codeberg_tools bundle gives the same read-only review surface on Codeberg and self-hosted Gitea. Agents draft the review against the real diff, then an engineer approves before any comment posts, because every write routes through a separate HITL gate.

The Security Auditor runs the ten-item OWASP checklist against each diff and flags CWE numbers, package_intel_tools surfaces stale or abandoned dependencies, and the DevOps Engineer scores CI/CD, secrets, observability, and DR posture. The Tech Lead then files every finding as a HITL-gated Jira or Linear ticket, so audit evidence accumulates weekly instead of stalling the window.