Comparison
Agentry vs LogRocket
LogRocket is the polished replay-first product: a top-tier replay player with Redux/Vuex/MobX state inspection, Core Web Vitals, mature mobile SDKs, and a UI built for teams who watch replays as a daily ritual. Agentry records session replays through PostHog and exposes them via an AI agent, with errors, analytics, and deploys queryable in the same prompt. Pick the workflow that matches how your team actually debugs frontend.
TL;DR
Pick LogRocket if
- Product, UX, or CX teams watch replays as their main debugging surface every day
- You need Redux / Vuex / MobX state inspection mid-replay
- Core Web Vitals + frontend performance monitoring is a first-class need
- Mobile native replay (iOS, Android, React Native) is on the requirements list
- Enterprise SSO + SOC 2 + audit log are mandatory
Pick Agentry if
- Replay is one tool among many — you also want errors, analytics, and deploys in the same plane
- Your debug workflow is "ask the agent in the editor" not "open the replay dashboard"
- You don't want to install LogRocket's SDK in your client
- "Pull this customer's replay and tell me what they did" sounds better than scrubbing manually
Feature comparison
| Capability | LogRocket | Agentry |
|---|---|---|
| Session replay | Yes — most polished player on the market | Yes — PostHog-backed, less polished UI |
| Frontend error capture | Yes — auto-instrumented | Yes — via raw POST or Sentry wire protocol |
| Redux / Vuex / MobX state inspection | Yes — inline in replay timeline | No |
| Core Web Vitals + frontend perf | Yes — first-class | No |
| Mobile SDKs (iOS / Android / RN) | Yes — mature | No |
| Product analytics (funnels, retention) | Limited | Full HogQL via agent |
| Deploy attribution | Limited (release tracking) | First-class, auto-correlated |
| Feature flags + A/B tests | No | Yes |
| Investigation surface | Web UI | AI agent in your editor |
| SDK install required | Yes — required for replay | PostHog snippet for replay; no SDK for errors |
| SSO + SOC 2 + audit log | Yes | Audit log yes through HTTP; no SSO/SOC 2 |
| Pricing model | Per-session tiers | All features on every plan; priced by events + retention |
When LogRocket is the right call
LogRocket is the right tool when session replay is the primary surface your team debugs through. Product managers, UX researchers, and customer-experience teams whose daily ritual is "watch replays, find the friction, file the ticket" get genuinely more out of LogRocket's player than anything else on the market. The Redux / Vuex / MobX state inspection mid-replay is unique — you scrub to a moment in the user's session and see the exact application state at that frame. Replays are also auto-grouped into "Galleries" of similar sessions, which makes spotting patterns across many users tractable.
LogRocket also wins on the frontend-performance edges: Core Web Vitals tracking, custom marks, and integrated network / console / performance timelines all in the same player. Mobile native SDKs (iOS, Android, React Native) are mature. SSO, SOC 2, and audit logs are in place for enterprise procurement. Agentry has none of those.
If your team's frontend debug ritual is centred on the replay dashboard, LogRocket is the better product. Replacing that with an agent-mediated workflow is a cultural change, not just a tooling one.
When Agentry is the right call
Agentry is the right tool when replay is one signal among many — not the only signal you debug through. If your workflow is "a customer reported a checkout error, pull their replay, but also check their analytics events and the last deploy that touched that endpoint," doing that across LogRocket + Mixpanel + Sentry is three tabs and manual time alignment. In Agentry, that's one prompt — the agent calls the distinct-id summary, session-replay list, and replay-snapshot endpoints in sequence and tells you what happened. The customer-investigation workflow walks through that exact loop.
Agentry's replay player is less polished than LogRocket's — we run on PostHog under the hood, which records faithfully but doesn't do Redux state inspection. So if you'd prefer watching the replay yourself, LogRocket is better. But if you'd prefer the agent watching it and summarising for you ("user clicked checkout, saw a validation error, retried twice, abandoned"), Agentry's design is the right shape.
Agentry also covers analytics, feature flags, deploys, and error monitoring as first-class data, queryable in the same plane. LogRocket touches some of these — but it's primarily a replay product, and treating analytics as a side feature shows.
Migrating from LogRocket
Replay capture in Agentry runs through the PostHog recording snippet,
configured by the agent from the HTTP docs. Errors can move to
Agentry's /v1/logs/ endpoint independently with
AGENTRY_PUBLIC_API_KEY bearer auth. Dual-record for a week so you
can compare replay fidelity and switch when you're confident.
These workflows cover the LogRocket use cases most frontend teams care about:
-
Diagnose a bug from the session replay, not the stack
Pull a customer's replay and have the agent summarise the 60s before the error.
-
Diagnose a customer complaint in 30 seconds
Combines replay, errors, and event history for one user — the workflow LogRocket centres around.
-
Find the bugs 5 people hit but no one reported
Errors-by-users-affected — what LogRocket's "Galleries" surface, asked in chat.
-
Find errors that only hit specific browsers
Frontend bug triage by environment — pull the replays from the affected segment.
-
Track engagement after a push notification
Funnel + replay sampling for users who hit the campaign — one query, one prompt.
-
Browse all customer-support workflows →
Every workflow for diagnosing a specific user's issue — Agentry's bread-and-butter replay use case.
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.
- 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.