## Summary
Consolidates the AI tool provider architecture by creating a single
`ToolProviderService` as the entry point for all tool generation. This
removes multiple intermediate services and simplifies the codebase.
## Changes
### New Architecture
- **`ToolProviderService`**: Single service for all tool generation
with:
- `getTools(spec)` - Get tools by category with permissions
- `getToolByType(type)` - Get specific tool for workflow execution
- **`ToolCategory` enum**: Declarative specification of tool types:
- `DATABASE_CRUD` - Record CRUD operations
- `ACTION` - HTTP requests, email sending, article search
- `WORKFLOW` - Workflow management tools
- `METADATA` - Object/field metadata tools
- `NATIVE_MODEL` - Model-specific tools (e.g., web search)
- **`ToolSpecification` type**: Clean API for requesting tools with
permissions
### Removed
- `AiToolsModule` - No longer needed
- `ToolService` - Logic inlined into ToolProviderService
- `ToolAdapterService` - Logic inlined into ToolProviderService
- `ToolRegistryService` - Logic inlined into ToolProviderService
### Updated
- All consumers (agents, chat, MCP, workflows) now use
`ToolProviderService`
- Test files updated accordingly
## Stats
- **547 insertions, 1146 deletions** (net ~600 lines removed)
- 4 services deleted
- 1 module deleted
## Testing
- [x] Typecheck passes
- [x] Lint passes
Validations :
- relations count (in common api)
- oneToMany relation nested count (in common)
- requested fields count (in gql)
- root resolver count (in gql)
- root resolver duplicates (in gql)
- specific complexity for metadata / nesting count (in gql)
In this PR, we are adding a new command to reset a message channel.
We are also refactoring a bit cursor reset as we had multiple
implementations at different places in the code base
---------
Co-authored-by: Charles Bochet <charles@twenty.com>
## Problem
buildEntityMetadatas in GlobalWorkspaceOrmManager is computationally
expensive and was running on every executeInWorkspaceContext call. This
method uses
TypeORM's EntitySchemaTransformer and EntityMetadataBuilder to build
metadata for all workspace entities (30-50+ objects with many fields
each).
The resulting EntityMetadata[] is not serialisable which means it cannot
be cached in Redis because they contain:
- Circular references
- Functions/methods
- References to the DataSource instance
## Solution
Extended the workspace cache system to support local-only caching, then
created a cache provider for entityMetadatas.
## Implementation details
Updated @WorkspaceCache decorator (workspace-cache.decorator.ts)
- Added localOnly?: boolean option to skip Redis storage for
non-serializable data
Created WorkspaceEntityMetadatasCacheService
- Computes entity metadatas from DB to avoid race condition, this is
acceptable
Simplified GlobalWorkspaceOrmManager
- Now fetches entityMetadatas from cache instead of rebuilding on every
call
Updated Workspace migration runner - the only entry point where metadata
can change
- Now invalidate the new 'entityMetadata' local cache when
shouldIncrementMetadataGraphqlSchemaVersion is true (== field/object
mutations)
# Introduction
Closes https://github.com/twentyhq/core-team-issues/issues/1980
In this PR we migrate the agent from v1 to v2.
## New FlatRoleTargetByAgentIdMaps
Derivated the `flatRoleTargetMaps` to be building a
`flatRoleTargetByAgentIdMaps` to ease retrieving a roleId to associate
to an agent
## Coverage
Added strong coverage on both failing and successful CRU agents
operations
---------
Co-authored-by: Weiko <corentin@twenty.com>
This PR improves the general UX and DX of boards, by modifying the query
effect to only use paged group by queries.
In this PR we implement two more things in the backend for group by
queries :
- Fixed ORDER BY in the PARTITION BY sub-query (this wasn't working
because it was applied in the main query, so it sorted randomly picked
records, which was a correct sort on an incorrect dataset returned by
the sub-query)
- Added offset paging in PARTITION BY
Miscellaneous, various bug fixes and improvements along the way :
- Throttled loading of cards to avoid React freeze
- Handling of drag & drop
- Handling of create / delete / update
- Reworked skeleton (the library slows down a lot with hundreds of
skeleton for a spinning effect that is hardly noticed)
- Fixed refetch of aggregate queries (I included the new group by
aggregates query we use in the existing refetch mechanism)
- Re-trigger queries on filters and sorts changes
- Unselect all record ids when deleting / restoring / detroying
- Fetch only groups that still have records to lighten the group by
query.
# What remains to be done
This is still a naïve fetch more implementation that will work for a few
fetch more rounds, but if you scroll and load say 200 cards per column
on a board, React will re-render all 200 cards of each column each time.
We would probably need to virtualize the board with paged queries as we
did for the table, this could be done after this PR but seems less
urgent.
What's nice is that this new query pattern is well designed for
virtualization also, drawing from our experience with table
virtualization, and adapted to a multi-column request pattern, like a
2:2 matrix of records, for our boards.
So the remaining work would be to design a UI solution for virtualizing
this matrix of records, which could be quite different from our table
virtualization mechanism.
## Context
We've recently introduced a new workspace cache service which now acts
as a cache access and local storage for all workspace related data,
deprecating the individual specific services.
- Better performance through multiple caching/fetching strategies
- Consistent data access patterns across the codebase
- Reduced redis queries through MGET/MSET/PIPELINE with multiple cache
keys
## Context
Deprecating the old objectMetadataMap type in favour of split flat
entities to match with our new caching.
In the long run, trying to achieve:
- Better performance through caching
- Consistent data access patterns across the codebase
- Reduced database queries
Now that everything is based on flat entities, which are cached, we can
finish the refactoring of workspace context cache which should already
improve performances.
Then the last step will be to consume that new cache in the new global
datasource to get rid of the many workspace datasources stored in the
server
## Summary
This PR introduces a comprehensive agent evaluation system and refactors
the AI module structure for better organization.
## Key Changes
### 🎯 Agent Evaluation System
- Added **Agent Turn Evaluation** entities, DTOs, and database schema
- New GraphQL mutations: `evaluateAgentTurn` and `runEvaluationInput`
- Added `evaluationInputs` field to Agent entity for storing test inputs
- New `AgentTurnGraderService` for automatic turn evaluation
- Added evaluation UI with new **Evals** and **Logs** tabs in agent
detail pages
### 🏗️ Entity & Module Refactoring
- Renamed `AgentChatMessage` → `AgentMessage` for clarity
- Consolidated chat entities: `AgentMessage`, `AgentTurn`, and
`AgentChatThread`
- Reorganized AI modules under `ai/` subdirectory structure
- Updated imports across codebase to reflect new module paths
### 🤖 New Agents & Roles
- Added **Dashboard Builder Agent** for dashboard creation and
management
- Added **Dashboard Manager Role** with appropriate permissions
- Updated role permissions to be more granular (users vs agents vs API
keys)
### 🔐 Permission System Updates
- Added `HTTP_REQUEST_TOOL` permission flag
- Updated Workflow Manager role permissions (restricted tool access)
- Enhanced permission flag types to differentiate between user/agent/API
key contexts
- Added `isRelevantForAgents`, `isRelevantForApiKeys`,
`isRelevantForUsers` to permission flags
### 📨 Message Role Enhancement
- Added `system` role to `AgentMessageRole` enum (alongside
user/assistant)
- Updated message handling to support system prompts
### 🎨 UI/UX Improvements
- New tabs in agent detail: **Evals** and **Logs**
- Added turn detail page: `/ai/agents/:agentId/turns/:turnId`
- Fixed text overflow in `SettingsListItemCardContent`
- Updated role applicability labels ("Assignable to Workspace Members")
### 🛠️ Technical Improvements
- Fixed Zod schema validation for UUID and Date fields (use string
validators)
- Updated `ToolRegistryService` to properly register HTTP tool with
permission flag
- Enhanced error handling in agent execution services
- Updated database migrations for new entity schema
## Database Migrations
- `1764210000000-add-system-role-to-agent-message.ts`
- `1764220000000-add-evaluation-inputs-to-agent.ts`
- `1764200000000-add-agent-turn-evaluation.ts`
- `1764100000000-refactor-agent-chat-entities.ts`
## Testing
- [ ] Agent evaluation flow tested
- [ ] Dashboard Builder agent tested
- [ ] Permission system validated
- [ ] UI tabs and navigation tested
- [ ] Database migrations run successfully
## Breaking Changes
⚠️ **Entity Rename**: `AgentChatMessage` renamed to `AgentMessage` -
GraphQL queries need updating
## Related Issues
<!-- Link any related issues here -->
## Screenshots
<!-- Add screenshots if applicable -->
## Overview
This PR replaces the dynamic agent handoff system with a more
predictable planning-based router that decides upfront how to handle
multi-agent coordination.
## Major Changes
### 🔄 Architecture Shift: Handoffs → Planning
**Removed:**
- `AgentHandoffEntity` and handoff tracking system
- `AgentHandoffService` and `AgentHandoffExecutorService`
- Dynamic agent-to-agent transfers during execution
- Handoff tool generation and description templates
**Added:**
- `AiRouterService` with two strategies: `simple` (single agent) and
`planned` (multi-agent)
- `AgentPlanExecutorService` for executing multi-step plans
- Plan validation (cycle detection, dependency resolution)
- `UnifiedRouterResult` type with discriminated union
### 🤖 New Standard Agents
Added two new specialized agents:
- **Researcher Agent**: Web search, fact-finding, competitive
intelligence
- **Code Agent**: TypeScript function generation for serverless
workflows
### 🏗️ Router Refactoring (Latest)
Split router responsibilities into focused services:
- `AiRouterStrategyDeciderService`: Decides simple vs planned strategy
- `AiRouterPlanGeneratorService`: Generates and validates execution
plans
- `AiRouterService`: Coordinates between services (reduced from 426→275
lines)
### ⚙️ Configuration Improvements
- Added `outputStrategy` to agent definitions (`direct` vs `synthesize`)
- Removed hardcoded special cases for workflow-builder
- Added `plannerModel` field to workspace entity
- Increased `MAX_STEPS` from 10 to 25 for complex workflows
### 📝 Agent Prompt Refinements
Significantly simplified prompts for better clarity:
- Workflow Builder: 51→36 lines
- Helper: 49→28 lines
- Data Manipulator: Enhanced with sorting guidance
### 🔍 Enhanced Debugging
- Plan reasoning and step count in data message parts
- Router debug info with token usage tracking
- Better logging throughout execution pipeline
## Benefits
1. **Simpler Mental Model**: Router decides upfront vs dynamic transfers
2. **Better Predictability**: Users see the plan before execution
3. **Cleaner Architecture**: SRP with focused services
4. **Configuration Over Code**: Agent behavior via config, not hardcoded
logic
5. **Plan Validation**: Catches invalid dependencies and cycles
## Migration Notes
- Database migration removes `agentHandoff` table
- Adds `plannerModel` column to workspace table
- No API breaking changes (agent endpoints unchanged)
## Testing
- Integration tests updated to remove handoff dependencies
- Agent tool test utilities simplified
- Plan validation covered by new logic
## Next Steps (Future PRs)
- Parallel execution of independent plan steps
- Dynamic re-planning based on results
- Plan caching for common routing patterns
- Error recovery strategies in plan executor
## Context
Deprecating legacy ObjectMetadata from cache in favor of flat entities.
Introducing utils to build byName/byNameSingular/byNamePlural in
isolated cases
## Next
- I had to introduce a util to build from flat to legacy
objectMetadataMaps, we should instead use flat maps directly when needed
(datasource, schema generation, etc)
- Deprecate metadata version in the cache
- Use the new cache strategy for flat entities with permissions and
feature flags and inject in the global datasource context
closes https://github.com/twentyhq/core-team-issues/issues/1629
To do before requesting review :
- filter update
Migration to come in an other PR
Strat :
1/ Null transformation
- [x] Transform NULL equivalent value to NULL in field validation in
common api - pre-query - with feature flag
- [ ] Same logic in ORM (Not done, complex to handle feature flag here)
- [x] Transform NULL value to equivalent in data formatting in ORM -
post-query
2/ Migration (in other PR) for fieldMetadata not nullable with default
defaultValue (empty string, ...)
- [ ] Remove NOT NULL db constraint
- [ ] Update record value to NULL
- [ ] Update field metadata : isNullable:true
- [ ] Update uniqueIndex whereClause (also for standard uniqueIndex)
- [ ] Activate feature flag
3/ Update metadata creation
- [x] No more default default value
- [x] Update standard field nullability
- [x] Remove index default whereClause for standard field
4/ Update filter
- [x] When filtering on NULL or empty string, be sure all records are
returned (the one with NULL + the one with "")
5/ Test
- [ ] Strat. to do
While investigating the issue a customer was facing, I discovered that
before records and after records could be in different order, making the
orm event and timeline activity engine mix records
Closes https://github.com/twentyhq/core-team-issues/issues/1891
Create empty buckets according to the date granularity
Video QA:
https://github.com/user-attachments/assets/86c0f817-35b3-4bab-b093-d11491684b82
Note: We would also need to create empty buckets for cyclic
granularities (DAY_OF_THE_WEEK, MONTH_OF_THE_YEAR, QUARTER_OF_THE_YEAR).
TODO:
- Always order the cyclic granularities Monday -> Sunday (take
firstDayOfTheWeek into account), January -> December, Q1 -> Q4. For now
they are returned by the backend in alphabetical order, which doesn't
make much sense
- Remove the translation into the user's locale of these granularities
from the backend because otherwise we can't reconstruct the missing days
or month in the frontend since they will be translated
---------
Co-authored-by: Lucas Bordeau <bordeau.lucas@gmail.com>
Fixes https://github.com/twentyhq/private-issues/issues/362
**How to reproduce**
Link a person that has a duplicate to an opportunity. (you can create a
duplicate by giving two people the same linkedin link).
Open the opportunity record from opportunity table.
Click on the related person (in "Point of contact").
In front of "Duplicates", click on the merge button (two arrows becoming
one).
You should here see an empty "Merge preview" and an error when clicking
"First" tab.
**Issue**
The issue is that CommandMenuMergeRecordPage is getting the referenced
objectMetadataItem from useContextStoreObjectMetadataItemOrThrow without
an instance id. contextStoreObjectMetadataItem is still "opportunity" as
it should be, being on an opportunity view.
So further down it attempts to display the record page according the
label identifier field from opportunity, which is a text field, "name",
and it breaks because the record is actually a person for who the "name"
field is not a text but a full_name type.
**Fix**
I suggested a fix that offers the possibility to find the referenced
objectMetadataItem from the MergeRecords instanceId. But I still gave
flexibility to avoid having to set that state everytime we open the
merge tab, by falling back to the default
contextStoreObjectMetadataItem. (this is used when we merge records from
ticking two records from a view and open the command menu).
Im not sure this is the best option. Open to suggestions !
---------
Co-authored-by: Charles Bochet <charles@twenty.com>
Closes https://github.com/twentyhq/core-team-issues/issues/1600.
Two remarks
- This `limit` variable does not reduce postgre's work at it still needs
to scan the whole table. It did not seem possible to me to optimize this
as we cannot foresee which dimensions will be used by the user, and an
optimization could only result from an index on the dimension(s) (e.g.:
group companies by addressCity limit 50 can be optimized if we have an
index on companies.addressCity + we had a default orderBy on
adressCity). But this will still optimize the FE which at the moment
receives all groups and truncates the result.
- I have not done the work on the FE as the addition of limit is a
breaking change, and will break until the workspaces' schema is rebuilt,
so we need to flush the cache. I think this could be acceptable as the
feature is in the lab but I preferred not doing it yet as it would have
no impact since in the BE I added a default limit to 50 groups, and I
expect more FE work will be done to allow the user to choose their own
limit
# Introduction
Remove the v2 feature flag for view-field field-metadata and
object-metadata metadata entities
## Some details
- Disabled nestjs-query for object metadata creation and explicitly
calling it
- removed all v1 integration tests files
## Remarks
Not remove v2 referencing in both filenaming right now will handle that
globally later
## Breaking change
Due to object metadata resolver createOne standardization had to rename
the input from `CreateObjectInput` to `CreateOneObjectInput`
Fixes https://github.com/twentyhq/twenty/issues/15809
## Context
We recently introduced a global datasource now consuming workspace
context from ALS store however the authContext was missing (only the
workspaceId was there) which "broke" event emission, now missing the
workspaceMemberId
This PR adds the missing authContext so we can access from anywhere in
the datasource. We are still passing it as a parameters on repository
level for legacy but in theory we should be able to remove it from
everywhere and consume the context
<img width="929" height="253" alt="Screenshot 2025-11-13 at 18 58 16"
src="https://github.com/user-attachments/assets/3e04f264-95e6-4831-94f3-fc01603f19bd"
/>
## Context
This PR introduces a Global Workspace DataSource that consolidates
workspace-specific database access through a single TypeORM DataSource
instance with AsyncLocalStorage-based context management instead of N
workspace datasources.
- Created GlobalWorkspaceDataSource extending TypeORM's DataSource to
manage multiple workspaces with entity metadata caching (1-hour TTL)
- Implemented AsyncLocalStorage for workspace context propagation
(WorkspaceContextForStorage) containing workspace ID, metadata,
permissions, and feature flags
- Modified query execution flow to wrap operations in workspace context
via GlobalWorkspaceOrmManager.executeInWorkspaceContext()
- Added schema name to entity schemas for proper multi-tenant database
separation
Next:
- use the new global workspace datasource everywhere and deprecate
workspace datasource factory
- improve metadata caching using a short TTL to avoid multiple calls to
redis
- Leverage the new WorkspaceContextALS and put it higher in the request
hierarchy to have access to permission, metadata and featureflag
everywhere --- build it manually for commands --- find a way to
propagate it in jobs?
- Remove PG_POOL patch once we have a unique datasource and increase
global datasource pool size
## Implementation
Why ALS:
1. Automatic Per-Request Isolation
With schema-based multi-tenancy, each workspace has its own PostgreSQL
schema
(e.g. workspace_20202020-1c25-4d02-bf25-6aeccf7ea419).
The critical challenge is ensuring that concurrent requests from
different tenants don't interfere with each other.
```typescript
// Request A (Workspace 1) and Request B (Workspace 2) executing concurrently
// Without ALS: Race condition — they'd share the same global state!
// With ALS: Each request has isolated context ✓
```
ALS automatically isolates context per async execution chain, so:
- Request from Tenant A → ALS stores workspaceId: "tenant-a" → Queries
hit workspace_tenant_a schema
- Request from Tenant B → ALS stores workspaceId: "tenant-b" → Queries
hit workspace_tenant_b schema
✅ No interference, even when executing simultaneously on the same
Node.js event loop.
2. No Manual Context Passing
Before ALS, you'd need to pass workspaceId through every function call:
```typescript
// ❌ Without ALS - Context threading nightmare
getRepository(workspaceId, entity)
→ createEntityManager(workspaceId)
→ getMetadata(workspaceId, target)
→ findInCache(workspaceId, cacheKey)
```
With ALS:
```typescript
// ✅ With ALS - Clean, implicit context
getRepository(entity) // Reads workspaceId from ALS
→ createEntityManager() // Reads workspaceId from ALS
→ getMetadata(target) // Reads workspaceId from ALS
→ findInCache(cacheKey) // Reads workspaceId from ALS
```
example
```typescript
override findMetadata(target: EntityTarget<ObjectLiteral>): EntityMetadata | undefined {
const context = getWorkspaceContext(); // 👈 Automatically gets the right workspace!
const { workspaceId, metadataVersion } = context;
const cacheKey = `${workspaceId}-${metadataVersion}`;
// ... returns metadata for THIS workspace's schema
}
```
3. Async Chain Propagation
Node.js operations are heavily async. ALS automatically propagates
context through:
- async/await chains
- Promise chains
- Callbacks
```typescript
executeInWorkspaceContext(workspaceId, async () => {
await prepareContext(); // Has context ✓
const results = await run(); // Has context ✓
await enrichResults(); // Has context ✓
// Even nested async operations maintain context!
await Promise.all([
saveToCache(), // Has context ✓
emitEvent(), // Has context ✓
logMetrics(), // Has context ✓
]);
});
```
5. Schema-Specific Metadata Caching
The implementation caches entity metadata per workspace + version:
```typescript
// Cache key format: "workspaceId-metadataVersion"
const cacheKey = `${workspaceId}-${metadataVersion}`;
```
Why this matters with schemas:
- Each workspace has different table structures (custom fields, objects)
- EntitySchema includes schema: "workspace_xxx" property
- Each cached metadata points to the correct schema
ALS ensures getWorkspaceContext() returns the right workspaceId,
so you always get the correct schema's metadata from cache.
6. Single DataSource for All Tenants
The key change here:
```typescript
// ❌ Old approach: One DataSource per tenant
const dataSourceTenantA = new DataSource({ schema: 'workspace_a' });
const dataSourceTenantB = new DataSource({ schema: 'workspace_b' });
// Problem: Hundreds of DB connection pools!
```
```typescript
// ✅ New approach: One shared DataSource + ALS context
const globalDataSource = new GlobalWorkspaceDataSource();
// ALS determines which schema to use at runtime
```
When you call:
```typescript
globalDataSource.getRepository('person');
```
It internally does:
```typescript
const context = getWorkspaceContext(); // Gets current tenant from ALS
const metadata = this.findMetadata('person'); // Finds metadata for THIS tenant's schema
// EntityMetadata includes: schema: "workspace_20202020-1c25..."
// TypeORM automatically queries: SELECT * FROM "workspace_20202020-1c25...".person
```
7. Request Lifecycle Example
```typescript
// 1. GraphQL request arrives: "query people { ... }"
// 2. Middleware extracts authContext.workspace.id = "tenant-a"
// 3. Query runner wraps execution in ALS:
executeInWorkspaceContext("tenant-a", async () => {
// 4. Everything inside has access to workspace context:
const repo = getRepository('person'); // ALS → tenant-a
const metadata = getMetadata('person'); // ALS → tenant-a → cache["tenant-a-v5"]
// 5. TypeORM builds query with correct schema:
// SELECT * FROM "workspace_tenant_a"."person" WHERE ...
// 6. Even nested calls work:
await saveAuditLog(); // ALS → tenant-a → correct audit schema
await emitWebhook(); // ALS → tenant-a → correct tenant webhook
});
// 7. Request completes, ALS context automatically cleaned up
```
8. Safety & Error Prevention
```typescript
// If you forget to set context:
const context = getWorkspaceContext();
// ❌ Throws: "Workspace context not set..."
// Fails fast rather than querying wrong schema!
// Can't accidentally query wrong tenant:
// Context is immutable within execution scope
```
## Overview
This PR strengthens our permission system by introducing more granular
role-based access control across the platform.
## Changes
### New Permissions Added
- **Applications** - Control who can install and manage applications
- **Layouts** - Control who can customize page layouts and UI structure
- **AI** - Control access to AI features and agents
- **Upload File** - Separate permission for file uploads
- **Download File** - Separate permission for file downloads (frontend
visibility)
### Security Enhancements
- Implemented whitelist-based validation for workspace field updates
- Added explicit permission guards to core entity resolvers
- Enhanced ESLint rule to enforce permission checks on all mutations
- Created `CustomPermissionGuard` and `NoPermissionGuard` for better
code documentation
### Affected Components
- Core entity resolvers: webhooks, files, domains, applications,
layouts, postgres credentials
- Workspace update mutations now use whitelist validation
- Settings UI updated with new permission controls
### Developer Experience
- ESLint now catches missing permission guards during development
- Explicit guard markers make permission requirements clear in code
review
- Comprehensive test coverage for new permission logic
## Testing
- ✅ All TypeScript type checks pass
- ✅ ESLint validation passes
- ✅ New permission guards properly enforced
- ✅ Frontend UI displays new permissions correctly
## Migration Notes
Existing workspaces will need to assign the new permissions to roles as
needed. By default, all new permissions are set to `false` for non-admin
roles.
**Before**
- any user with workpace_members permission was able to remove a user
from their workspace. This triggered the deletion of workspaceMember +
of userWorkspace, but did not delete the user (even if they had no
workspace left) nor the roleTarget (acts as junction between role and
userWorkspace) which was left with a userWorkspaceId pointing to
nothing. This is because roleTarget points to userWorkspaceId but the
foreign key constraint was not implemented
- any user could delete their own account. This triggered the deletion
of all their workspaceMembers, but not of their userWorkspace nor their
user nor the roleTarget --> we have orphaned userWorkspace, not
technically but product wise - a userWorkspace without a workspaceMember
does not make sense
So the problems are
- we have some roleTargets pointing to non-existing userWorkspaceId
(which caused https://github.com/twentyhq/twenty/issues/14608 )
- we have userWorkspaces that should not exist and that have no
workspaceMember counterpart
- it is not possible for a user to leave a workspace by themselves, they
can only leave all workspaces at once, except if they are being removed
from the workspace by another user
**Now**
- if a user has multiple workspaces, they are given the possibility to
leave one workspace while remaining in the others (we show two buttons:
Leave workspace and Delete account buttons). if a user has just one
workspace, they only see Delete account
- when a user leaves a workspace, we delete their workspaceMember,
userWorkspace and roleTarget. If they don't belong to any other
workspace we also soft-delete their user
- soft-deleted users get hard deleted after 30 days thanks to a cron
- we have two commands to clean the orphans roleTarget and userWorkspace
(TODO: query db to see how many must be run)
**Next**
- once the commands have been run, we can implement and introduce the
foreign key constraint on roleTarget
Fixes https://github.com/twentyhq/twenty/issues/14608
As title
- adds decorators in twenty-sdk
- update twenty-cli load-manifest to it gets @FieldMetadata infos +
testing
- update twenty-server so it CRUD fields properly, using
universalIdentifier
- Fix UI so we can update managed objects records
- move FieldMetadata items from twenty-server to twenty-shared
## Description
- This PR approaches to solve
https://github.com/twentyhq/twenty/issues/15201
- updated `createDryRunResponse` to return fully populated merged record
- this way the frontend can render the populated data it as-is without
recomputing relations
- Dry-run now uses the same nested-relations population path as the
non-dryRun flow
---------
Co-authored-by: Etienne <45695613+etiennejouan@users.noreply.github.com>
This PR implements the necessary tools to have `react-datepicker`
calendar and our date picker components work reliably no matter the
timezone difference between the user execution environment and the user
application timezone.
Fixes https://github.com/twentyhq/core-team-issues/issues/1781
This PR won't cover everything needed to have Twenty handle timezone
properly, here is the follow-up issue :
https://github.com/twentyhq/core-team-issues/issues/1807
# Features in this PR
This PR brings a lot of features that have to be merged together.
- DATE field type is now handled as string only, because it shouldn't
involve timezone nor the JS Date object at all, since it is a day like a
birthday date, and not an absolute point in time.
- DATE_TIME field wasn't properly handled when the user settings
timezone was different from the system one
- A timezone abbreviation suffix has been added to most DATE_TIME
display component, only when the timezone is different from the system
one in the settings.
- A lot of bugs, small features and improvements have been made here :
https://github.com/twentyhq/core-team-issues/issues/1781
# Handling of timezones
## Essential concepts
This topic is so complex and easy to misunderstand that it is necessary
to define the precise terms and concepts first. It resembles character
encoding and should be treated with the same care.
- Wall-clock time : the time expressed in the timezone of a user, it is
distinct from the absolute point in time it points to, much like a
pointer being a different value than the value that it points to.
- Absolute time : a point in time, regardless of the timezone, it is an
objective point in time, of course it has to be expressed in a given
timezone, because we have to talk about when it is located in time
between humans, but it is in fact distinct from any wall clock time, it
exists in itself without any clock running on earth. However, by
convention the low-level way to store an absolute point in time is in
UTC, which is a timezone, because there is no way to store an absolute
point in time without a referential, much like a point in space cannot
be stored without a referential.
- DST : Daylight Save Time, makes the timezone shift in a specific
period every year in a given timezone, to make better use of longer days
for various reasons, not all timezones have DST. DST can be 1 hour or 30
min, 45 min, which makes computation difficult.
- UTC : It is NOT an “absolute timezone”, it is the wall-clock time at
0° longitude without DST, which is an arbitrary and shared human
convention. UTC is often used as the standard reference wall-clock time
for talking about absolute point in time without having to do timezone
and DST arithmetic. PostgreSQL stores everything in UTC by convention,
but outputs everything in the server’s SESSION TIMEZONE.
## How should an absolute point in time be stored ?
Since an absolute point in time is essentially distinct from its
timezone it could be stored in an absolute way, but in practice it is
impossible to store an absolute point in time without a referential. We
have to say that a rocket launched at X given time, in UTC, EST, CET,
etc. And of course, someone in China will say that it launched at 10:30,
while in San Francisco it will have launched at 19:30, but it is THE
SAME absolute point in time.
Let’s take a related example in computer science with character
encoding. If a text is stored without the associated encoding table, the
correct meaning associated to the bits stored in memory can be lost
forever. It can become impossible for a program to guess what encoding
table should be used for a given text stored as bits, thus the glitches
that appeared a lot back in the early days of internet and document
processing.
The same can happen with date time storing, if we don’t have the
timezone associated with the absolute point in time, the information of
when it absolutely happened is lost.
It is NOT necessary to store an absolute point in time in UTC, it is
more of a standard and practical wall-clock time to be associated with
an absolute point in time. But an absolute point in time MUST be store
with a timezone, with its time referential, otherwise the information of
when it absolutely happened is lost.
For example, it is easier to pass around a date as a string in UTC, like
`2024-01-02T00:00:00Z` because it allows front-end and back-end code to
“talk” in the same standard and DST-free wall-clock time, BUT it is not
necessary. Because we have date libraries that operate on the standard
ISO timezone tables, we can talk in different timezone and let the
libraries handle the conversion internally.
It is false to say that UTC is an absolute timezone or an absolute point
in time, it is just the standard, conventional time referential, because
one can perfectly store every absolute points in time in UTC+10 with a
complex DST table and have the exactly correct absolute points in time,
without any loss of information, without having any UTC+0 dates
involved.
Thus storing an absolute point in time without a timezone associated,
for example with `timestamp` PostgreSQL data type, is equivalent to
storing a wall-clock time and then throwing away voluntarily the
information that allows to know when it absolutely happened, which is a
voluntary data-loss if the code that stores and retrieves those
wall-clock points in time don’t store the associated timezone somewhere.
This is why we use `timestamptz` type in PostgreSQL, so that we make
sure that the correct absolute point in time is stored at the exact time
we send it to PostgreSQL server, no matter the front-end, back-end and
SQL server's timezone differences.
## The JavaScript Date object
The native JavaScript Date object is now officially considered legacy
([source](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Date)),
the Date object stores an absolute point in time BUT it forces the
storage to use its execution environment timezone, and one CANNOT modify
this timezone, this is a legacy behavior.
To obtain the desired result and store an absolute point in time with an
arbitrary timezone there are several options :
- The new Temporal API that is the successor of the legacy Date object.
- Moment / Luxon / @date-fns/tz that expose objects that allow to use
any timezone to store an absolute point in time.
## How PostgreSQL stores absolute point in times
PostgreSQL stores absolute points in time internally in UTC
([source](https://www.postgresql.org/docs/current/datatype-datetime.html#DATATYPE-DATETIME-INPUT-TIME-STAMPS)),
but the output date is expressed in the server’s session timezone
([source](https://www.postgresql.org/docs/current/sql-set.html)) which
can be different from UTC.
Example with the object companies in Twenty seed database, on a local
instance, with a new “datetime” custom column :
<img width="374" height="554" alt="image"
src="https://github.com/user-attachments/assets/4394cb43-d97e-4479-801d-ca068f800e39"
/>
<img width="516" height="524" alt="image"
src="https://github.com/user-attachments/assets/b652f36a-d2e2-47a4-8950-647ca688cbbd"
/>
## Why can’t I just use the JavaScript native Date object with some
manual logic ?
Because the JavaScript Date object does not allow to change its internal
timezone, the libraries that are based on it will behave on the
execution environment timezone, thus leading to bugs that appear only on
the computers of users in a timezone but not for other in another
timezone.
In our case the `react-datepicker` library forces to use the `Date`
object, thus forcing the calendar to behave in the execution environment
system timezone, which causes a lot of problems when we decide to
display the Twenty application DATE_TIME values in another timezone than
the user system one, the bugs that appear will be of the off-by-one date
class, for example clicking on 23 will select 24, thus creating an
unreliable feature for some system / application timezone combinations.
A solution could be to manually compute the difference of minutes
between the application user and the system timezones, but that’s not
reliable because of DST which makes this computation unreliable when DST
are applied at different period of the year for the two timezones.
## Why can’t I compute the timezone difference manually ?
Because of DST, the work to compute the timezone difference reliably,
not just for the usual happy path, is equivalent to developing the
internal mechanism of a date timezone library, which is equivalent to
use a library that handles timezones.
## Using `@date-fns/tz` to solve this problem
We could have used `luxon` but it has a heavy bundle size, so instead we
rely here on `@date-fns/tz` (~1kB) which gives us a `TZDate` object that
allows to use any given timezone to store an absolute point-in-time.
The solution here is to trick `react-datepicker` by shifting a Date
object by the difference of timezone between the user application
timezone and the system timezone.
Let’s take a concerte example.
System timezone : Midway, ⇒ UTC-11:00, has no DST.
User application timezone : Auckland, NZ ⇒ UTC+13:00, has a DST.
We’ll take the NZ daylight time, so that will make a timezone difference
of 24 hours !
Let’s take an error-prone date : `2025-01-01T00:00:00` . This date is
usually a good test-case because it can generate three classes of bugs :
off-by-one day bugs, off-by-one month bugs and off-by-one year bugs, at
the same time.
Here is the absolute point in time we take expressed in the different
wall-clock time points we manipulate
Case | In system timezone ⇒ UTC-11 | In UTC | In user application
timezone ⇒ UTC+13
-- | -- | -- | --
Original date | `2024-12-31T00:00:00-11:00` | `2024-12-31T11:00:00Z` |
`2025-01-01T00:00:00+13:00`
Date shifted for react-datepicker | `2025-01-01T00:00:00-11:00` |
`2025-01-01T11:00:00Z` | `2025-01-02T00:00:00+13:00`
We can see with this table that we have the number part of the date that
is the same (`2025-01-01T00:00:00`) but with a different timezone to
“trick” `react-datepicker` and have it display the correct day in its
calendar.
You can find the code in the hooks
`useTurnPointInTimeIntoReactDatePickerShiftedDate` and
`useTurnReactDatePickerShiftedDateBackIntoPointInTime` that contain the
logic that produces the above table internally.
## Miscellaneous
Removed FormDateFieldInput and FormDateTimeFieldInput stories as they do
not behave the same depending of the execution environment and it would
be easier to put them back after having refactored FormDateFieldInput
and FormDateTimeFieldInput
---------
Co-authored-by: Charles Bochet <charles@twenty.com>
