This PR delivers the first slice of the voice channel: inbound call
handling. When a customer calls a configured voice
number, Chatwoot now creates a new conversation and shows a dedicated
call bubble in the UI. As the call progresses
(ringing, answered, completed), its status updates in real time in both
the conversation list and the call bubble, so
agents can instantly see what’s happening. This focuses on the inbound
flow and is part of breaking the larger voice
feature into smaller, functional, and testable units; further
enhancements will follow in subsequent PRs.
references: #11602 , #11481
## Testing
- Configure a Voice inbox in Chatwoot with your Twilio number.
- Place a call to that number.
- Verify a new conversation appears in the Voice inbox for the call.
- Open it and confirm a dedicated voice call message bubble is shown.
- Watch status update live (ringing/answered); hang up and see it change
to completed in both the bubble and conversation
list.
- to test missed call status, make sure to hangup the call before the
please wait while we connect you to an agent message plays
## Screens
<img width="400" alt="Screenshot 2025-09-03 at 3 11 25 PM"
src="https://github.com/user-attachments/assets/d6a1d2ff-2ded-47b7-9144-a9d898beb380"
/>
<img width="700" alt="Screenshot 2025-09-03 at 3 11 33 PM"
src="https://github.com/user-attachments/assets/c25e6a1e-a885-47f7-b3d7-c3e15eef18c7"
/>
<img width="700" alt="Screenshot 2025-09-03 at 3 11 57 PM"
src="https://github.com/user-attachments/assets/29e7366d-b1d4-4add-a062-4646d2bff435"
/>
<img width="442" height="255" alt="Screenshot 2025-09-04 at 11 55 01 PM"
src="https://github.com/user-attachments/assets/703126f6-a448-49d9-9c02-daf3092cc7f9"
/>
---------
Co-authored-by: Muhsin <muhsinkeramam@gmail.com>
This PR adds the foundation for account-level SAML SSO configuration in
Chatwoot Enterprise. It introduces a new `AccountSamlSettings` model and
management API that allows accounts to configure their own SAML identity
providers independently, this also includes the certificate generation
flow
The implementation includes a new controller
(`Api::V1::Accounts::SamlSettingsController`) that provides CRUD
operations for SAML configuration
The feature is properly gated behind the 'saml' feature flag and
includes administrator-only authorization via Pundit policies.
There was a fundamental difference in how resolution counts were
calculated between the agent summary and timeseries reports, causing
confusion for users when the numbers didn't match.
The agent summary report counted all `conversation_resolved` events
within a time period by querying the `reporting_events` table directly.
However, the timeseries report had an additional constraint that
required the conversation to currently be in resolved status
(`conversations.status = 1`). This meant that if an agent resolved a
conversation that was later reopened, the resolution action would be
counted in the summary but not in the timeseries.
This fix aligns both reports to count resolution events rather than
conversations in resolved state. When an agent resolves a conversation,
they should receive credit for that action regardless of what happens to
the conversation afterward. The same logic now applies to bot
resolutions as well.
The change removes the `conversations: { status: :resolved }` condition
from both `scope_for_resolutions_count` and
`scope_for_bot_resolutions_count` methods in CountReportBuilder, and
updates the corresponding test expectations to reflect that all
resolution events are counted.
## About timezone
When a timezone is specified via `timezone_offset` parameter, the
reporting system:
1. Converts timestamps to the target timezone before grouping
2. Groups data by local day/week/month boundaries in that timezone, but
the primary boundaries are sent by the frontend and used as-is
3. Returns timestamps representing midnight in the target timezone
This means the same events can appear in different day buckets depending
on the timezone used. For summary reports, it works fine, since the user
only needs the total count between two timestamps and the frontend sends
the timestamps adjusted for timezone.
## Testing Locally
Run the following command, this will erase all data for that account and
put in 1000 conversations over last 3 months, parameters of this can be
tweaked in `Seeders::Reports::ReportDataSeeder`
I'd suggest updating the values to generate data over 30 days, with
10000 conversations, it will take it's sweet time to run but then the
data will be really rich, great for testing.
```
ACCOUNT_ID=2 ENABLE_ACCOUNT_SEEDING=true bundle exec rake db:seed:reports_data
```
Pro Tip: Don't run the app when the seeder is active, we manually create
the reporting events anyway. So once done just use `redis-cli FLUSHALL`
to clear all sidekiq jobs. Will be easier on the system
Use the following scripts to test it
- https://gist.github.com/scmmishra/1263a922f5efd24df8e448a816a06257
- https://gist.github.com/scmmishra/ca0b861fa0139e2cccdb72526ea844b2
- https://gist.github.com/scmmishra/5fe73d1f48f35422fd1fd142ea3498f3
- https://gist.github.com/scmmishra/3b7b1f9e2ff149007170e5c329432f45
- https://gist.github.com/scmmishra/f245fa2f44cd973e5d60aac64f979162
---------
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
Co-authored-by: Pranav <pranav@chatwoot.com>
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Currently, auto-assignment runs only during conversation creation or
update events. If no agents are online when new conversations arrive,
those conversations remain unassigned.
With this change, unassigned conversations will be automatically
assigned once agents become available. The job runs every 15 minutes and
uses a fair distribution threshold of 100 to prevent a large number of
conversations from being assigned to a single available agent. This will
be customizable later.
We were using UTM params on various branding urls which weren't
compliant to standard utm params and hence were ignored by analytics
tooling. this PR ensures that the params stays compliant with defined
standard
ref: https://en.wikipedia.org/wiki/UTM_parameters
## Changes
- updated utm tags on widget and survey urls
- added utm on helpcenter branding
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
We now support searching within the actual message content, email
subject lines, and audio transcriptions. This enables a faster, more
accurate search experience going forward. Unlike the standard message
search, which is limited to the last 3 months, this search has no time
restrictions.
The search engine also accounts for small variations in queries. Minor
spelling mistakes, such as searching for slck instead of Slack, will
still return the correct results. It also ignores differences in accents
and diacritics, so searching for Deja vu will match content containing
Déjà vu.
We can also refine searches in the future by criteria such as:
- Searching within a specific inbox
- Filtering by sender or recipient
- Limiting to messages sent by an agent
Fixes https://github.com/chatwoot/chatwoot/issues/11656
Fixes https://github.com/chatwoot/chatwoot/issues/10669
Fixes https://github.com/chatwoot/chatwoot/issues/5910
---
Rake tasks to reindex all the messages.
```sh
bundle exec rake search:all
```
Rake task to reindex messages from one account only
```sh
bundle exec rake search:account ACCOUNT_ID=1
```
## Linear reference:
https://linear.app/chatwoot/issue/CW-4649/re-imagine-assignments
## Description
This PR introduces the foundation for Assignment V2 system by
implementing agent_capacity and their association with inboxes and
users.
## Type of change
- [ ] New feature (non-breaking change which adds functionality)
## How Has This Been Tested?
Test Coverage:
- Controller specs for assignment policies CRUD operations
- Enterprise-specific specs for balanced assignment order
- Model specs for community/enterprise separation
## Checklist:
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented on my code, particularly in hard-to-understand
areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged and published in downstream
modules
---------
Co-authored-by: Pranav <pranav@chatwoot.com>
Added comprehensive Twilio WhatsApp content template support (Phase 1)
enabling text, media, and quick reply templates with proper parameter
conversion, sync capabilities.
**Template Types Supported**
- Basic Text Templates: Simple text with variables ({{1}}, {{2}})
- Media Templates: Image/Video/Document templates with text variables
- Quick Reply Templates: Interactive button templates
Front end changes is available via #12277
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
There were customer reported issues with FAQs which were generated in a
different langauge than what they were expecting. The reason behind this
was that the language of the account was not considered in the prompt
provided. If the language of the content was say Spanish, and the
account locale was english. The output was not predicable. The output
depends on the model and the execution time.
This PR would update the prompt to behave consistently with the account
locale. Even though the content provided is in a different language, it
would generate FAQs in the account locale.
Changes:
- Updated the prompt to include a detailed expectation of the FAQs
quality along with the language
- Added specs for the services where the prompt generator is called.
Tested the prompt using Phoenix playground across GPT 5, GPT 4.1, GPT
4.0. The reasoning setting for GPT 5 needs to be low so that it doesn't
generate random questions like "What was this updated?"
