Provide tokenized session context to your AI agents

Every action that happens inside of your app or on your website, easily summarized and consumed by an AI agent at scale. The agent reads every session and flags the moments worth a human's attention: errors, rage clicks, and abandoned flows.

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session-context.stream
live
ses_3a9f2c1·2m 14s·web

User abandoned signup at payment step after card-validation error returned twice on the same input.

form_errorabandonedbilling
ses_8b2c47e·0m 47s·ios

User browsed the demo gallery for under a minute, scrolled to the pricing section, and left before opening any item.

bouncepricing
ses_5e1d09a·4m 02s·web

User reached the checkout step, received a 500 error when submitting the order, and opened the support chat with the full session attached.

error_5xxsupport_handoff

For products that allow users to build for other users.

What output does it give?

Summarize what a user has done
Every action a user took during the session is captured and condensed into a short narrative that an AI agent can consume directly.
Summarize what the user has seen
The actual UI states the user encountered are captured alongside the actions, so the agent can reason about what was on screen at each moment.
Analyze the likely objective of a user and whether or not they achieved it
The session is interpreted as a goal-directed sequence, with a judgement on whether the user reached their objective and the evidence behind that judgement.
Highlight frustrations or blockers that prevented them from achieving their goal
Rage clicks, dead clicks, repeated retries, and silent failures are surfaced as concrete moments tied to the part of the journey where they occurred.
Combine technical errors with actual user journeys into a code-ready issue
Stack traces are fused with the user journey that triggered them, producing an issue description that contains enough context to start working on a fix.

What's different about rrweb AI?

Each session is layered into multiple datatypes, with the highest layer optimized for minimal token usage. Data can be selectively stored depending on how useful it is to an AI, so each layer is kept only when an agent will actually use it.

Session payload, top to bottom
Summary tokens
AI-ready · ~hundreds of tokens
~0.5 KB
Tokenized events
Structured actions, errors, surfaces
~8 KB
Full session timeline
Frame-accurate rrweb stream
~200 KB
Lower token consumption
The summary layer fits in a few hundred tokens, so an agent can hold context for many more sessions in a single call.
Higher fidelity
The tokenized event layer preserves every meaningful action, error, and surface the user encountered, with the full structure of the journey intact.
Full timeline access
When an agent needs to verify what happened at a specific moment, it can drop into the raw rrweb stream and inspect the frame directly.

What can you use it for?

issues / ai-generated
bugAI-generated
Card-validation error blocks first-time signup
form_errorbillingP1
uxAI-generated
Pricing tier hover state is hard to distinguish on mobile
mobilepricingP3
bugAI-generated
Checkout returns 500 when promo + gift card combined
checkouterror_5xxP0

The product feedback loop, with and without AI

The two versions of the loop start from the same product and the same set of users. What changes is how much information is available about each session and how quickly the team can act on it.

Create a product
Have users test it
Have some users complain but most drop off
Change a few things and try again

Sign up to become a design partner

rrweb AI is currently being developed alongside a small group of teams that build products on top of session data. If you would like your team to be part of that group, please get in touch and we will set you up with early access.

Become a design partner

We respond to design partner enquiries within one business day.