Human Behavior
AI session replay that watches every session, triages the bugs it finds, and opens pull requests to fix them.
Human Behavior earns its place when your team already pays engineers to scrub replays and triage bug reports — the shipped auto-fix pipeline (2.11.0) turns a detected issue into a GitHub App PR, and the bug inbox ranks P0–P2 with dollar risk, so value shows up as merged pull requests rather than another unopened dashboard. Pick it over PostHog or Amplitude if automated detection and autonomous follow-through matter more than experiments, feature flags, and a long-retention warehouse. Skip it if your code review would block any agent-authored PR, or if you need self-hosting.
Verified 4d ago · liveness 78/100 · cite: rightaichoice.com/tools/human-behavior
- Product and engineering teams that already spend hours scrubbing replays
- Startups wanting replays, logs, traces, and web analytics from one SDK snippet
- Growth teams that need overnight funnel-drop alerts in Slack
- Support and CX leads who need affected users named with context when something breaks
- Teams that need feature flags, experiments, and surveys in the same tool
- Organizations requiring self-hosted or on-premise deployment of session data
- Companies whose code-review policy would block any agent-authored PR
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Skip Human Behavior if you need feature flags, experiments, and a long-retention warehouse in one tool, or if your code review would block any agent-authored pull request.
The Team plan is quoted at $588/yr in the product's own demo data — annual commitment rather than a monthly figure, so budget for the full year up front.
Public pricing wasn't visible in this run's sources, so treat the plan structure as something to confirm directly. The Team plan appears in the product's own demo data at $588/yr. Against PostHog you are paying for the AI detection and auto-fix layer on top of comparable signal collection; against Amplitude you are trading suite breadth for autonomous follow-through. Budget-conscious startups that only need replays and funnels can get a lower bill elsewhere; the spend makes sense once the agent
In short
Human Behavior — AI session replay that watches every session, triages the bugs it finds, and opens pull requests to fix them. Best for Product and engineering teams that already spend hours scrubbing replays, Startups wanting replays, logs, traces, and web analytics from one SDK snippet, Growth teams that need overnight funnel-drop alerts in Slack. Free to use.
What's new in Human Behavior
Checked 4 days agoAcross the latest 5 updates: 2 feature updates, 2 launches and 1 changelog entry.
2.12.0 — marketing homepage and Win95 experience
Version 2.12.0 ships the finished marketing homepage alongside a Windows 95–style landing page at /win95 with draggable section windows, a native pricing window, and a working system tray.
2.11.5 — funnel bar and funnel-shape visualizations
Adds funnel bar and funnel-shape visualizations with builder UI cleanup and an empty-state fix, and isolates staging deploys from the production IAM role.
2.11.4 — dashboard caching and demo tour sync
Dashboard responsiveness improves with stale-while-revalidate caching across agent lists, member counts, insights, monitoring, and issues overview, plus a public /demo tour synced to the current UI.
2.11.3 — public dashboard share links, SMS sender pools
Adds public dashboard share links, sidebar agent slots, a per-agent From-number pool with first-text contact cards for SMS, and fixes live replay freezing on long sessions.
2.11.0 — auto-fix pipeline ships
A first-party cloud coding agent starts from a detected issue, builds a fix briefing from stored evidence, authenticates as a GitHub App, and opens PRs gated on blast-radius guardrails, verification levels, and an attempt cap.
What people actually say about Human Behavior — is it worth it?
We scanned public community sources for Human Behavior on Aug 13, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Human Behavior? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- DOM-level session replay with console logs, network traces, and errors on one timeline
- One SDK snippet captures replays, logs, traces, web analytics, and Core Web Vitals
- AI watches every replay to surface bugs, rage clicks, dead ends, and silent errors
- Bug inbox with P0–P2 triage, users affected, and dollar risk per issue
- Cloud coding agent builds a fix briefing from stored evidence and opens GitHub App pull requests
- PR blast-radius guardrails, verification levels, and an attempt cap
- Agents file Linear tickets and post context into Slack channels
- Natural-language query interface that recomputes funnels and answers with measured numbers
- Funnel analytics with drop-off step detection and always-on conversion monitoring
- Funnel bar and funnel-shape visualizations with builder UI (2.11.5)
- Slack alerts that name the funnel step that got worse after a deploy
- User profiles auto-enriched from work email with company, role, plan, and location
- Cross-session, cross-device user identification and timelines
- Web analytics and Core Web Vitals tracked alongside every session
- Public dashboard share links for stakeholder review (2.11.3)
About Human Behavior
Human Behavior is an AI session replay and product analytics platform for teams that have stopped watching replays by hand. One SDK snippet captures session replays, console logs, network traces, web analytics, and performance vitals on a single timeline — the vendor pitches parity with PostHog for signal collection, with nothing else to wire up. You get pageviews, funnels, identified users, and replays in the same dashboard rather than stitching four tools together. The AI layer is the switch reason. It reads every replay the way an engineer would, flagging broken flows, rage clicks, dead ends, and silent errors users never report, then groups and ranks them into a bug inbox with P0 through P2 triage, users affected, and dollar risk attached. Agents do the follow-through: the first-party cloud coding agent shipped in version 2.11.0 starts from a detected issue, builds a fix briefing from stored evidence, authenticates as a GitHub App, and opens a pull request gated on blast-radius guardrails, verification levels, and an attempt cap. Beyond code, agents file Linear tickets and post context into Slack. Two other pieces carry weight. Funnels are monitored continuously — when conversion drops, the tool names the step that got worse and posts to Slack, and a natural-language interface answers questions like whether the checkout redesign moved Pay clicks, with measured numbers. User profiles enrich automatically from the work email, filling company, role, plan, and location with no extra code. Recent releases added funnel bar and funnel-shape visualizations (2.11.5), public dashboard share links and per-agent SMS From-number pools (2.11.3), and stale-while-revalidate dashboard caching (2.11.4). The company is ex-Meta and ex-Google Brain alumni, backed by $5M from General Catalyst, Y Combinator, and Vercel. Versus PostHog or Amplitude you are buying automated detection and autonomous follow-through rather than a broader analytics suite — there is no feature-flag or experiment layer here, and no raw SQL warehouse on decade-long retention.
Behind the Verdict
The honest framing of Human Behavior is that it is two products sharing a foundation. The foundation is a session-replay and web-analytics collector: one SDK sends replays, console logs, network traces, vitals, and pageviews onto one timeline, inputs are masked in the browser before data leaves the client, and identity resolution stitches a visitor's sessions and devices together so "Brave Falcon" becomes devon.park@meridian.io with company, role, plan, and location enriched from the work email. That part is table stakes and the vendor says so, claiming parity with PostHog for signal collection rather than superiority. The second product is the AI layer, and it is where the differentiation actually lives. Replays get read automatically for broken flows, rage clicks, dead ends, and silent errors, then written into a bug inbox with P0–P2 triage, users affected, and dollar risk (the demo issue shows 12 users and $4.2k at risk). Agents act on that: version 2.11.0 shipped the auto-fix pipeline, a first-party cloud coding agent that builds a fix briefing from stored evidence, authenticates as a GitHub App, and opens PRs gated on blast-radius guardrails, verification levels, and an attempt cap. Linear tickets and Slack posts round out the follow-through. Funnel monitoring is the quieter win — when conversion drops it names the step that got worse and posts to Slack, which is more useful at 3am than a chart someone has to notice. The weaknesses are real. There is no feature-flag, experimentation, or survey surface, so teams that outgrow basic analytics will still pay for a second tool. Session-data residency is a problem for regulated buyers: nothing in the material describes a self-hosted or on-premise deployment, and the changelog mentions a 90-day purge for ended contracts rather than long archival retention. Teams with strict human-only code review will find the agent-authored PR interesting but unusable. And the enrichment story depends on corporate email — consumer products where people sign up with a Gmail address get little from the profile layer. The release cadence is genuinely fast and mostly substantive rather than cosmetic: 2.11.1 hardened billing with dunning lockout and entitlement guards plus a public subprocessors page, 2.11.2 hardened deploys and added keyset event pagination, 2.11.3 fixed live replay freezing on long sessions and internal users leaking into live-user counts, 2.11.4 cut dashboard load with stale-while-revalidate caching. Some releases are marketing-page work (2.12.0 shipped the Win95 landing page), which is fine but shouldn't be mistaken for product depth. If you want the detection-and-repair loop and can live without a warehouse, this is a coherent buy; if analytics breadth is the requirement, it is a companion tool, not a replacement.
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Real-world workflow fit
Concrete scenarios for the personas Human Behavior actually fits — and what changes day-one when you adopt it.
Installs the SDK on a Tuesday, watches the bug inbox populate overnight, and walks into standup with a P0 list ranked by users affected rather than a folder of replays.
Outcome: The checkout dead-click issue arrives already reproduced in 9 of 12 sessions with a dollar-risk figure attached, so the team sizes the fix before writing a line of code.
Points the auto-fix agent at the top P0, reviews the generated fix briefing built from stored session evidence, and checks the blast-radius guardrails before the GitHub App opens the PR.
Outcome: A draft pull request lands with the failing flow and evidence attached instead of a vague ticket, and review starts from a concrete diff.
Monitors the activation funnel around the clock and asks the natural-language interface whether last week's pricing-page redesign changed how many people click Pay.
Outcome: When a step regresses post-deploy, Slack names the step that got worse, and the answer to the redesign question comes back as measured funnel numbers rather than an opinion.
Use Cases
- Catch checkout dead-clicks on a specific browser and get a draft PR with the fix
- Ask "did the redesign change Pay clicks?" and get measured funnel numbers, not a guess
- Get a ranked P0–P2 bug list with users affected and dollar risk instead of a noisy inbox
- Alert Slack the moment a funnel step degrades after a deploy
- Correlate Sentry exceptions with the sessions that hit them
- Auto-file Linear tickets pre-loaded with the affected users and replay evidence
- Identify anonymous visitors as named accounts with company, role, and plan enrichment
- Share a public dashboard link so stakeholders can review activation metrics without a login
Models Under the Hood
as of 2026-09-23
Limitations
- No feature-flag, experimentation, or survey surface — this is not a full product-analytics suite, so many teams will run it alongside PostHog or Amplitude.
- Nothing in the published material describes self-hosted or on-premise session-data deployment; the changelog notes a 90-day data purge for ended contracts rather than long archival retention, which will concern regulated buyers and analysts who want raw SQL over years of history.
- Profile enrichment depends on work-email signals, so consumer products get little from it.
- Agent-authored GitHub PRs are gated on blast-radius guardrails, verification levels, and an attempt cap, and assume your review process accepts machine-written code at all.
- The product ships very frequently (2.10.x through 2.12.x in roughly five weeks), which means surface and behavior change often.
as of 2026-10-04
Verification history
We have re-verified Human Behavior 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Human Behavior's pricing actually pencils out — and where peers do it cheaper.
Public pricing wasn't visible in this run's sources, so treat the plan structure as something to confirm directly. The Team plan appears in the product's own demo data at $588/yr. Against PostHog you are paying for the AI detection and auto-fix layer on top of comparable signal collection; against Amplitude you are trading suite breadth for autonomous follow-through. Budget-conscious startups that only need replays and funnels can get a lower bill elsewhere; the spend makes sense once the agent
Setup time & first value
How long it actually takes to get something useful out of Human Behavior — broken out by persona, not the marketing-page minute.
Solo dev or small team: about 15–30 minutes — drop the humanbehavior-js snippet into your app layout with replays, logs, traces, and vitals enabled, and events stream within seconds. Product manager with no code access: budget a day to get engineering to ship the SDK, then same-day visibility into the dashboard. Larger orgs adding agents: allow a few days — the GitHub App install, Linear and
Switching to or from Human Behavior
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From PostHog: the vendor claims signal-collection parity, so run both SDKs briefly, verify replay and funnel counts match, then drop the PostHog snippet.
- →From FullStory or LogRocket: enable replays and console capture first, compare a week of session volume, then retire the old recorder.
- →From manual replay review: point the AI at existing traffic and use the first week's bug inbox as the baseline before changing process.
- →From Sentry-only triage: connect Sentry so exceptions link to the sessions that hit them, then shift issue creation to the bug inbox.
- ↗To PostHog: export analytics events and stand up PostHog's own replay capture before retiring the Human Behavior SDK.
- ↗To Amplitude or Mixpanel: keep Human Behavior running through one full retention window so historical funnels stay queryable during the switch.
- ↗To a self-hosted recorder: plan the move ahead of an ended contract, since data is purged 90 days after the contract ends.
- ↗To plain Sentry plus a warehouse: extract issue and event data before cancelling, as no raw warehouse export is described in the public material.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Human Behavior”, and we withheld 4: 4 did not mention Human Behavior. Showing the 2 we can prove are about Human Behavior.
Official links
Tools that pair well with Human Behavior
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Featured Head-to-Head Comparisons
Human Behavior vs Truleo
Choose Truleo if you need to connect siloed law enforcement data (jail calls, BWC, RMS) and automate case lead generation. Choose Human Behavior if you want AI agents to analyze session replays and automatically fix bugs in your product. They serve completely different domains—no overlap.
Human Behavior vs Presto Voice
Presto Voice is the clear choice for QSR chains seeking to automate drive-thru ordering and boost revenue, backed by proven partnerships like Dairy Queen and Taco John's. Human Behavior is a powerful analytics tool for product teams wanting autonomous session replay analysis and bug fixing, but it's not designed for restaurant operations. Choose based on your industry: restaurants vs. digital products.
Human Behavior vs Screenplayiq
If you're a screenwriter or producer needing data-driven box office predictions from scripts, ScreenplayIQ is your tool. For product and UX teams wanting AI agents that autonomously analyze session replays and even fix bugs, Human Behavior is revolutionary. They serve entirely different markets—choose based on your domain.
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Frequently Asked Questions
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