Comparison
Agentry vs Highlight.io
Closest positioning competitor — both bundle errors, session replay, and product analytics. Highlight adds distributed tracing + full-text log aggregation and is open-source (self-hostable). Agentry is managed-only and agent-first: the AI agent in your editor IS the dashboard.
TL;DR
Pick Highlight.io if
- You need distributed tracing (OpenTelemetry-compatible) and full-text log aggregation alongside errors and replay
- You need self-hosted / open-source for compliance, cost, or data-residency reasons
- Your team's workflow is "open the dashboard, watch the replay, read the trace" — Highlight's web UI is polished and mature
- You ship native mobile apps and need iOS / Android SDKs
- You need a comparison-mature product with longer track record
Pick Agentry if
- Your team debugs from an AI coding agent (Claude Code, Cursor, Codex, Cline, Windsurf) and the dashboard is friction
- You want a smaller scope (errors + analytics + deploys, no log aggregation, no tracing) and less to configure
- You don't want to install an SDK — raw
fetchis enough - You want first-class public embeddable dashboards via a read-only key
- You're a small-to-mid engineering team where the agent-first UX outweighs raw feature breadth
Feature comparison
| Capability | Highlight.io | Agentry |
|---|---|---|
| Error monitoring | Yes | Yes |
| Session replay | Yes — polished player, state inspection | Yes — PostHog-backed |
| Product analytics | Yes — built-in | Yes — HogQL on PostHog |
| Distributed tracing (OpenTelemetry) | Yes | No |
| Log aggregation (full-text search) | Yes | Structured-event only |
| Frontend perf (Core Web Vitals) | Yes | No |
| Mobile SDKs (iOS/Android) | Yes | No (raw HTTP works) |
| Self-hosted / open-source | Yes — MIT, source on GitHub | No — managed-only |
| Deploy attribution | Limited | First-class |
| AI-assisted | Yes — error grouping/summaries in their dashboard | The AI agent is the dashboard |
| SDK install required | Yes (replay + tracing need their SDK) | No — fetch only |
| Pricing | Open-source / per-session paid tier | All features on every plan; priced by events + retention |
When Highlight.io is the right call
Highlight is the right tool when you need the broader observability surface — distributed tracing (OpenTelemetry-native), backend log aggregation with full-text search, and frontend performance monitoring — bundled with errors and replay. That's a meaningfully larger product than Agentry covers, and Highlight does it well.
Open-source is the second strong reason. The Highlight code is on GitHub under MIT; you can self-host for data-residency, compliance, cost-control, or audit reasons. If "we need to run this on our infrastructure" is a procurement gate, Agentry (managed-only) won't clear it. Use Highlight.
Highlight's web UI is also more mature — longer-lived product, more polish, AI-assisted error grouping in the dashboard itself. If your team's workflow centres on watching replays and reading traces in a browser tab, that's where Highlight earns its keep.
When Agentry is the right call
Agentry is the right tool when your team already debugs from an AI coding agent (Claude Code, Cursor, Codex, Cline, Windsurf). The primary interface is the HTTP API your agent calls, not a web dashboard. You ask the agent "what broke after my last deploy?" — it pulls cases, queries analytics, reads recent deploys, and drafts the fix in the same conversation. Highlight has a polished dashboard; Agentry doesn't because it's not pointing at one.
Agentry also wins on install footprint. Highlight requires their
SDK for session replay and tracing to work; Agentry's entire
instrumentation is a ~25-line fetch helper your agent
generates at install time. No language SDK to keep up to date,
no version migrations.
And if you don't need tracing or log aggregation — most product engineering teams don't, day-to-day — Agentry's smaller scope is an advantage. Less to learn, less to configure, less monthly billing pressure to keep using all the features.
Migrating from Highlight.io
If you don't actually use Highlight's tracing or log-aggregation
features (a lot of teams turn them on, never look at the data,
keep paying), the path to Agentry is dual-writing errors +
analytics events for a week. Agentry's three POST endpoints
(/v1/logs/, /v1/analytics/,
/v1/deploys/) cover the signals most teams actually
query. Browser and server signals use their own scoped credentials;
deploys use a CI credential and stable Idempotency-Key after a
successful release. If after a week you miss tracing, stay on Highlight.
These workflows cover the use cases Highlight users care about:
-
Find what broke after your last deploy
Diff error fingerprints around the latest deploy SHA — what Highlight's "Releases" view does, but agent-investigated.
-
Diagnose a bug from the session replay, not the stack
Pull a user's replay + analytics events from one prompt — Highlight's "watch the replay" workflow, in chat.
-
Diagnose a customer complaint in 30 seconds
Combines errors + analytics + replay tied to one distinct_id.
-
Find the bugs 5 people hit but no one reported
Issues sorted by distinct users affected, surfaced in chat.
-
Watch staging — page only on genuinely new errors
Hourly Routine that diffs new fingerprints since the last run.
-
Browse all error-monitoring workflows →
Every workflow that covers patterns Highlight or Sentry users will recognise.
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.