Comparisons
Compare Agentry to other tools.
Honest side-by-side comparisons against the tools Agentry overlaps with. Each page lists when the competitor is the right pick, when Agentry is, and what a migration path actually looks like.
We say "use Sentry instead" when Sentry is the right answer. We say "use Datadog" when you need full observability. The trustworthy framing is the whole point — if we lied here, you'd catch it the moment you installed.
- Error monitoring read →
Agentry vs Sentry
Both capture errors. Sentry has a polished web dashboard; Agentry has an AI agent in your editor.
- Product analytics + replay + flags read →
Agentry vs PostHog
Same HogQL backend (Agentry runs PostHog under the hood). PostHog has the UI; Agentry exposes it through your agent and adds first-class error monitoring.
- Product analytics read →
Agentry vs Mixpanel
Both do funnels, retention, cohorts. Mixpanel's UI is for analysts; Agentry's interface is your AI agent — and it includes errors + deploys.
- Product analytics read →
Agentry vs Amplitude
Amplitude has the polished UI for PMs and behavioural cohorts. Agentry exposes the same query layer through your AI agent, plus errors + deploys.
- Observability read →
Agentry vs Datadog
Datadog covers infra + APM + logs + RUM at enterprise scale and price. Agentry covers errors + analytics + deploys, agent-investigated, for product engineering teams.
- Feature flags + experimentation read →
Agentry vs LaunchDarkly
LaunchDarkly has mature per-language SDKs and enterprise approval workflows. Agentry has flags + analytics + errors in one product, agent-driven, with a smaller scope.
- Session replay + frontend read →
Agentry vs LogRocket
LogRocket has the most polished replay player in the category. Agentry has replay (PostHog-backed) + errors + analytics + deploys queryable together from your AI agent.
- Errors + replay + tracing read →
Agentry vs Highlight.io
Closest positioning competitor. Highlight adds distributed tracing + logs and is open-source. Agentry is managed-only and agent-first, with smaller scope.
How to read these
Comparison pages are usually marketing surfaces. Every "we win" checkmark with a "they lose" X. We did the opposite: each page has a "pick the competitor if…" section that genuinely recommends against Agentry when it doesn't fit.
Concretely: don't use Agentry if you need SOC 2 / HIPAA today. Don't use it if you need full APM with distributed tracing. Don't use it if your team doesn't use AI coding agents — the value proposition collapses without one. Those statements live in the relevant comparison pages and at the top of /agentry.md.
What Agentry IS for: teams that already debug from Claude Code, Cursor, Codex, etc., who want errors + analytics + deploys in one product their agent can investigate, and who don't want to install an SDK to get started.
Make up your own mind.
Install in 5 minutes, dual-write to whatever you're already using for a week, compare. If Agentry isn't better for your team, no harm done.
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.
- 1. Open your repo in Codex, Claude Code, Cursor etc.
- 2. Paste the install prompt.
- 3. Your agent reads the install doc and shows you an implementation plan for approval.