Comparison
Agentry vs PostHog
Both run on the same query engine (HogQL) and cover the same surface: product analytics, session replay, feature flags, surveys, A/B tests. PostHog is built around a polished web UI for analysts; Agentry exposes those same capabilities through an AI agent in your editor, with first-class error monitoring layered in. Pick the interface model that matches how your team works.
TL;DR
Pick PostHog if
- You have a dedicated analytics team that lives in PostHog's UI all day
- You need data warehouse sync (Snowflake, BigQuery, Redshift)
- You rely on web autocapture and don't want to instrument events explicitly
- Stakeholders need to self-serve dashboards without involving an engineer
Pick Agentry if
- The same person who builds the product also queries the data
- You want errors and analytics in one product, queryable together
- Your "analyst" is increasingly your AI agent
- You don't want to operate PostHog yourself (Agentry manages it)
Feature comparison
| Capability | PostHog | Agentry |
|---|---|---|
| Product analytics (HogQL) | Yes — native | Yes — same HogQL, queried via agent |
| Error monitoring | Basic add-on | First-class (cases, fingerprints, suppressions) |
| Session replay | Yes — native | Yes — PostHog-backed, included |
| Feature flags, cohorts, surveys, A/B tests | Yes | Yes — same surface via the HTTP API |
| Data warehouse sync | Yes (Snowflake, BigQuery, Redshift) | No |
| Web autocapture | Yes | No — explicit instrumentation only |
| Investigation surface | Web UI (mature, broad) | Agent in your editor |
| Self-hosted option | Yes | No — managed only |
| SDK install required | Yes (posthog-js, posthog-node, etc.) | No — ~25 lines of fetch |
| Deploy attribution | No | Yes — first-class signal |
| Pricing model | Free tier + per-event after threshold | All features on every plan; priced by events + retention |
When PostHog is the right call
PostHog is the right tool when product analytics is a job your organisation does, not a question your engineers ask in passing. If you have product managers, growth analysts, or marketers who need to build funnels, slice cohorts, and publish dashboards themselves — without filing a ticket — PostHog's UI is what they want. It's been polished over years specifically for that workflow, and self-service is genuinely good.
PostHog also wins when you need its data platform features: warehouse sync to Snowflake / BigQuery / Redshift, web autocapture for marketing sites where you can't reasonably instrument every click, and the self-hosted option for teams with data residency requirements. Agentry doesn't have any of those.
If your stakeholders need to log into a product analytics tool and get answers without engineering involvement, that's PostHog. Routing those questions through an agent in someone's editor is the wrong shape.
When Agentry is the right call
Agentry is the right tool when the person asking the analytics question is also the person writing the code — typical of small-to-medium engineering teams where there is no separate analyst role. In that workflow, opening a dashboard tab is friction. You want to ask "how many users hit the new pricing page and then bounced?" in the same Cursor / Claude Code conversation you're already in, get the HogQL result, and have the agent draft the follow-up A/B test.
Agentry uses PostHog under the hood for analytics, replay, and flags — so the underlying capability surface is the same. The difference is the interface: HTTP API workflows instead of a web UI, and errors as a first-class signal in the same query plane (PostHog treats errors as a separate add-on, which makes "did the deploy that caused this error also cause the funnel drop?" awkward in their model).
Agentry also handles the PostHog operational layer for you — provisioning, project isolation, key management. If you'd rather not run PostHog yourself and don't need warehouse sync, that's a real saving.
Migrating from PostHog
Agentry's /v1/analytics/ endpoint accepts the same
event, distinct_id, and
properties shape. Your existing event names and property
keys keep working. You don't have to migrate all at once; dual-write
to both for a week, compare what you get, then cut over.
These workflows cover the PostHog use cases most product teams care about:
-
Build a signup funnel without opening a dashboard
PostHog's funnel insight, but the HogQL is written by your agent in chat.
-
Find the action that predicts activation
Equivalent of a PostHog correlation analysis, surfaced inline as a query result.
-
Spot accounts about to churn before they do
Behavioural cohort + ranking, the way a PostHog analyst would build it.
-
Analyze an existing product experiment
Verify exposure and outcome signals, compare exact cohorts, and prepare a reviewed decision memo.
-
Pull session replays that match an error
PostHog's replay product, but the agent picks the right session and summarises it.
-
Browse all PostHog-alternative workflows →
Every workflow tagged with patterns where Agentry replaces PostHog usage.
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.