Comparison

Agentry vs Sentry

Both capture errors. Sentry is built for a human-facing dashboard you open to triage; Agentry is built for an AI coding agent that triages and fixes errors directly in your editor. Use whichever matches how your team actually debugs.

TL;DR

Pick Sentry if

  • Your team has a dedicated SRE/on-call rotation reading a dashboard daily
  • You need 10+ years of battle-tested SDKs across 20+ frameworks
  • You're already on Sentry and the integration cost outweighs the agent-first benefits
  • Compliance/procurement requires a Fortune-500-listed vendor

Pick Agentry if

  • You debug from Claude Code / Cursor / Codex — the dashboard is friction
  • You want errors + analytics + deploys in one product, agent-investigated
  • You're building greenfield and don't want to ship an SDK
  • "Just ask the agent what broke" sounds better than "open the Sentry tab"

Feature comparison

Capability Sentry Agentry
Error capture + grouping Yes — 30+ SDKs Yes — raw HTTP POST (no SDK)
Stack unmangling (sourcemaps) Server-side, opaque HTTP sourcemap endpoint with provenance
Investigation surface Web dashboard Agent in your editor
Product analytics (funnels, retention) Limited (separate product) First-class (HogQL)
Deploy attribution Releases (manual setup) First-class, auto-correlated
Session replay Yes (separate paid SKU) Yes (PostHog-backed, included)
Feature flags + A/B tests No Yes
SDK install required Yes No — ~25 lines of fetch
Public-shareable dashboards Internal only Yes (CORS-open public URLs)
Pricing model Per-event tiers, gets expensive at scale All features on every plan; priced by events + retention
Setup time ~1 hour (SDK install + sourcemaps + releases) ~5 minutes (one prompt to your agent)

When Sentry is the right call

Sentry is the right tool when your debugging workflow already centres on a shared dashboard that on-call engineers and customer support open multiple times a day. The volume of mature SDKs (Python, Java, .NET, mobile native, etc.) is hard to match — if you're shipping in a framework that's been around since 2014, Sentry probably has a plug-and-play integration. The web UI's triage features (assignments, comments, status workflows, Slack integration) are mature.

Stick with Sentry if your team's debug ritual is "open the dashboard, find the issue, click resolve." Switching to agent-first debugging is a cultural change as much as a tooling one.

When Agentry is the right call

Agentry is the right tool when your team already lives in an AI coding agent (Claude Code, Cursor, Codex, Cline, Windsurf). In that workflow, opening a separate dashboard is friction — you want to ask the agent "what broke after my last deploy?" and have it investigate, query analytics, read the case, and draft the fix in the same conversation it's already in.

Agentry also wins when you want errors + analytics + deploys in one product. Sentry sells these as three SKUs; Agentry treats them as the same data plane, queryable in the same prompt. The deploy-regression workflow illustrates: one prompt diffs error fingerprints around the latest deploy and tells you what's new — Sentry can do the diff, but only in the dashboard.

And if you're building greenfield without an SDK install already in place: Agentry's no-SDK design is meaningfully simpler.

Try Agentry against your real data.

Dual-write for a week. Compare. Switch if it's better. The agent handles install — you just paste one prompt.

Agentry gives coding agents production context from errors, product analytics, and deploys through one HTTP API. Fetch and read https://agentry.sh/install.md completely, then install Agentry's full first-time telemetry baseline in this repository. I authorize device-code login, saving the owner key only to ~/.agentry/credentials.json, creating one Agentry project for this repository, and read-only inspection of shipped source plus hidden CI/provider configuration. Deeply analyze the product's business logic and complete value flow before choosing telemetry. Save the complete source-backed plan and show me its exact contents and hash. Until I explicitly approve that plan or request a replacement, do not edit app/CI source, place runtime/CI credentials, commit, exercise product traffic, start proof, push, or deploy. After approval, I authorize only the reviewed targets: place the required scoped browser/server/CI credentials through the established environment or secret mechanism, preserve existing telemetry, implement and test the baseline, commit it, push that reviewed commit when the shipped CI/provider path requires it, exercise safe proof paths with test/non-customer data, and perform one deployment through the reviewed shipped CI/provider path. Ask first if proof would charge money, contact a third party, change real customer data, or require new external access. After the plan is saved, immediately before every onboarding state-changing POST, GET current onboarding state, perform only its single returned next_action, then read state again; do not batch or infer later stages. Continue until status is verified, installation_complete is true, and next_action is null. Keep all secrets, source snapshots, proof markers, and scratch files outside the repository.

+ Full access
5.5 Extra High
  1. 1. Open your repo in Codex, Claude Code, Cursor etc.
  2. 2. Paste the install prompt.
  3. 3. Your agent reads the install doc and shows you an implementation plan for approval.