Bug reports become pull requests
Kai Code turns bug reports into pull requests for your engineers to review, and can start on its own when you allow it.
Compare · Tore vs Gleap
Gleap fixes what users report.Tore starts before they report it.
Gleap is the closest tool to Tore: support, in-app bug reports and an AI that turns reports into pull requests. Tore runs that loop too, and also starts from your error stream, so the fix can be under way before the first report.
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Same bug, two ways · Beacon Data search
Example dataWith Gleapsupport + bug reports + code agent
Maya · Beacon Data · bug reportSearch shows nothing when I filter by date.
AI agentThanks, Maya. I have the report and replay, and I am looking into it.
Pull request opened from the reportfor your engineers to review
Fix work started from the first reportafter Maya wrote in
With Toresupport + engineering, one thread
Error grouped from your error trackerDateRangeError · 1 group
Tore AIInvestigation opened from the error stream, before anyone wrote in.
Root cause foundsearch/query.ts:64 · date sent as local time
Fix written, tests pass+3 −1 · 18 tests
Maya · Beacon Data · bug reportSearch shows nothing when I filter by date.
Tore AIThanks, Maya. We found this one already and a fix is with our engineer now.
Approved by Alex · Maya told it is fixedsame thread
Where Gleap is strong,and when to pick it instead.
Gleap is the tool most like Tore, and it ships the report to pull request loop today. If your bugs come from what users report, it is a genuinely good choice.
In-app bug reporting and replay
Users report bugs from inside your app, with session replay to see what happened.
An AI agent on many channels
Kai answers on chat, email, WhatsApp, Instagram, Messenger, Telegram, Slack and the phone, and investigates hard tickets.
Feedback, roadmap and release notes
A feedback board, public roadmap, release notes, help center and surveys in the same product.
Pick Gleap when
Your fixes start from what users report, and you do not need investigations to start from your error stream.
- Most of your bugs surface through user reports, not your error tracker.
- You want in-app bug reporting, replay and a code agent from one vendor today.
- You want WhatsApp, Instagram or Telegram support alongside it.
- Your error tracker already covers what you need on the engineering side.
Tore vs Gleap,across the support to fix loop.
Both answer customers, capture bugs and write fixes. The difference is where the work starts and how the loop closes.
Tore
- Support inbox: yes
- In-app bug capture: yes
- Starts from your error stream: yes
- Investigates your code: yes
- Opens the pull request: yes
- Feedback board and roadmap: yes
- Tells the reporter it shipped: yes
Gleap
- Support inbox: yes
- In-app bug capture: yes
- Starts from your error stream: no
- Investigates your code: yes
- Opens the pull request: yes
- Feedback board and roadmap: yes
- Tells the reporter it shipped: partly
| Step | Gleap | Tore |
|---|---|---|
| Answers customers | Kai answers on chat, email, social, Slack and the phone. | Answers from your help center with the article cited, across email, chat widget and Slack. |
| Hands over to a person | A shared inbox for your team, alongside Kai. | Hands over with the full conversation when it is not confident. Anyone can take over at any moment. |
| Bug capturereplay, console, network | In-app bug reporting, with session replay. | Reports from the chat widget arrive with replay, console and network attached. Bug capture |
| Investigation in your code | Kai investigates hard tickets. | Reads the repository you connect and finds the root cause, down to the file and line. |
| Fix and tests | Kai Code writes the change from the bug report. | Writes the fix and runs your tests. Your engineer can request changes in plain words. |
| Pull request for approval | Opens a pull request for your engineers to review. | Preps the pull request. By default it waits for your engineer's approval. |
| Customers told it is fixed | Kai can update the customer in the conversation when a fix is live. | Everyone who reported it is told automatically when the fix is live. |
| Model choice | Model choice on some plans. | The right model for each job, from Claude, Codex, Gemini, Grok and more. You can set it per task. |
| Proactive errors | Starts from bug reports and approved work | Errors from your error tracker are grouped by fingerprint and can open an investigation before anyone writes in. |
Based on Gleap public pages (gleap.ai), checked 2026-10-30. Products change: if we have something wrong, tell us and we will correct it. For plans and pricing, see Tore pricing.
Desks answer but don't fix.Coding agents fix but never talk.
Most teams keep support conversations in one tool and the code in another, joined by a person copying between them. Gleap and Tore both join the two. Tore also listens to your error tracker.
Support desk
Talks to the customer
Intercom, Zendesk and help desks like them. Great at the conversation, then the bug leaves the thread.
- Answers and hands over
- Reads your code
- Writes the fix
Tore
One thread, both sides
The conversation, the investigation and the reply live together, so nothing is retyped.
- Answers and hands over
- Reads your code
- Writes the fix
Coding agent
Works in your code
AI engineers that start from an issue. Strong at the fix, but the customer never hears from them.
- Answers and hands over
- Reads your code
- Writes the fix
Where Gleap fits: it spans both sides for bugs that users report, with Kai Code turning reports into pull requests. Tore also starts from your error stream, so an investigation can be open before the first report.
The proactive half
It starts before anyone writes in.
Connect your error tracker and Tore groups errors by fingerprint. When a group crosses your threshold, it opens one investigation, within the budget you set.
- One bad deploy is one investigation, not ten thousand
- An ignore list and a stop switch, and support keeps working
- The same thread picks up the customer reports when they arrive
app.tore.ai/beacon-data/errors
Example dataDateRangeError in searchsearch/query.ts · after deploy 7c2e91a
2,340 eventsGrouped by fingerprint1 issue · threshold reached
1 group1 investigation openedbefore the first customer report
AI budget this month9% used
Questions people askbefore switching.
Is Gleap ahead of Tore on anything?
Yes. Gleap ships the report to pull request loop today, and it supports channels such as WhatsApp, Instagram and Telegram. If your bugs mostly come from user reports, it is a strong choice.
What does starting from the error stream add?
Many bugs never get reported, or get reported late. Tore groups the errors your error tracker already sees, opens one investigation per group, and links the customer reports to it as they come in.
Does Tore change our code without asking?
By default it waits for your engineer's approval. Your engineer can request changes in plain words, and Tore revises the fix and re-runs the tests. You set how much it does on its own, per repository, in AI controls.
Which AI models does Tore use?
The right model for each job: a fast model for quick answers, deep reasoning for root causes, a coding model for fixes. You can let Tore choose or set it per task. During early access, you can also bring your own AI keys.
How does the price compare?
Plans, what is included and how the AI is billed are on the pricing page.
Keep comparing.
Fix what users report,and what they never mention.
Tore answers your customers, captures the bug and starts from your error stream, all the way to a fix for your engineer to approve.