## Summary
- Add code interpreter tool that enables AI to execute Python code for
data analysis, CSV processing, and chart generation
- Support for both local (development) and E2B (sandboxed production)
execution drivers
- Real-time streaming of stdout/stderr and generated files
- Frontend components for displaying code execution results with
expandable sections
## Code Quality Improvements
- Extract `getMimeType` to shared utility to reduce code duplication
between drivers
- Fix security issue: escape single quotes/backslashes in E2B driver env
variable injection
- Add `buildExecutionState` helper to reduce duplicated state object
construction
- Add `DEFAULT_CODE_INTERPRETER_TIMEOUT_MS` constant for consistency
- Fix lingui linting warning and TypeScript theme errors in frontend
## Test Plan
- [ ] Test code interpreter with local driver in development
- [ ] Test code interpreter with E2B driver in production environment
- [ ] Verify streaming output displays correctly in chat UI
- [ ] Verify generated files (charts, CSVs) are uploaded and
downloadable
- [ ] Test file upload flow (CSV, Excel) triggers code interpreter
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Updates generated i18n catalogs for Polish and pseudo-English, adding
strings for code execution/output (code interpreter) and various UI
messages, with minor text adjustments.
>
> - **Localization**:
> - **Generated catalogs**: Refresh `locales/generated/pl-PL.ts` and
`locales/generated/pseudo-en.ts`.
> - Add strings for code execution/output (e.g., code, copy code/output,
running/waiting states, download files, generated files, Python code
execution).
> - Include new UI texts (errors, prompts, menus) and minor text
corrections.
> - No changes to `pt-BR`; other files unchanged functionally.
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
befc13d02c21e5a6647bc1aa6daa2a89f60b7ef8. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
## Context
Following https://github.com/twentyhq/twenty/pull/16399
Now using the new global orm manager everywhere and returning a
GlobalDatasource/WorkspaceDatasource based on a feature flag.
This means we now need to wrap all our ORM calls within
executeInWorkspaceContext callback (at least for now) so the global
datasource can dynamically hydrate its context via the new store (the
global datasource does not store anything related to workspaces as it is
now a unique singleton). If feature flag is off it still uses local data
stored in the workspace datasource.
## Context
Deprecating TwentyORMManager in favor of TwentyORMGlobalManager
(temporarily, as this will simplify the ultimate goal to later replace
all usages with the new TwentyORMGlobalManagerV2 which will have a
similar signature)
This means this PR had to refactor a bit of code to pass down the
workspaceId when not available directly as it is now a requirement,
meaning we also deprecated scopedWorkspaceContextFactory to have a less
obscure way to fetch the workspaceId and have something more
declarative.
Step 3 will be to update TwentyORMGlobalManager to use a featureFlag
toggling and use the new GlobalWorkspaceOrmManager internally using the
new cache service
Step 4 will be to remove the feature flag and pg_pool patch
## 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
- if >5000 workflows per hour, new ones should failed
- if >100 workflow per min, new ones should be set as not started.
Except manual trigger
- when enqueued, we check if there a not started workflows that may be
queued. If yes, we call the associated job
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
## 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
## Summary
This PR adds configurable response format support for AI agents,
allowing them to return either plain text or structured JSON data based
on a defined schema.
## Key Features
### 1. Agent Response Format Configuration
- Added `AgentResponseFormat` type supporting:
- `text`: Returns plain text responses (default)
- `json`: Returns structured JSON based on defined schema
- New `AgentResponseSchema` type moved to `twenty-shared/ai` for sharing
between frontend/backend
### 2. Settings UI
- New `SettingsAgentResponseFormat` component for configuring response
format
- Visual schema builder for defining JSON output structure
- Real-time validation and preview
- Integrated into agent settings tab
### 3. Workflow Integration
- AI Agent workflow action automatically uses agent's configured
response format
- Output schema dynamically generated from agent's response format
- Workflow variable picker shows structured fields for JSON responses
- Backward compatible with existing text-only agents
### 4. Backend Implementation
- Added `convertAgentSchemaToZod` utility to validate JSON responses
- Agent executor service handles both text and JSON generation
- Automatic agent creation/cloning when adding AI agent steps to
workflows
- Unique agent naming with conflict resolution
### 5. Database Migration
- Migration `1763622159656-update-agent-response-format.ts`
- Sets default `responseFormat` to `{"type":"text"}` for existing agents
- Updated all standard agents with proper response format
## Changes by Module
### Frontend (`twenty-front`)
- 🆕 `AgentResponseFormat` type
- 🆕 `SettingsAgentResponseFormat` component
- ✏️ Updated `WorkflowEditActionAiAgent` to support response format
configuration
- 🗑️ Removed deprecated `useAiAgentOutputSchema` hook and
`AiAgentOutputSchema` type
### Backend (`twenty-server`)
- 🆕 `AgentResponseFormat` type in agent entity
- 🆕 `convertAgentSchemaToZod` utility for schema validation
- ✏️ Updated `AiAgentExecutorService` to handle both text and JSON
generation
- ✏️ Updated `WorkflowSchemaWorkspaceService` to generate output schema
from agent config
- ✏️ Enhanced `WorkflowVersionStepOperationsWorkspaceService` with agent
creation/cloning
- 🆕 Agent naming constants for conflict resolution
### Shared (`twenty-shared`)
- 🆕 `AgentResponseSchema` type
- 🆕 `ModelConfiguration` type moved to shared package
- Updated exports in `ai/index.ts`
## Code Quality
- Removed useless comments following code style guidelines
- All linter checks passed
- Type-safe implementation with proper TypeScript types
## Testing
- ✅ Database migration tested
- ✅ Agent creation/cloning in workflows verified
- ✅ Response format switching (text ↔ JSON) validated
- ✅ Backward compatibility with existing agents confirmed
## Migration Notes
- Existing agents will have `responseFormat: {type: 'text'}` set
automatically
- No breaking changes - all existing functionality preserved
- Agents can be updated to use JSON format through settings UI
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>
We do not want the fields to update multiselect for upsert record
action. We want all available fields displayed by default.
This makes upsert record action closer to create record than update
record.
This PR:
- deletes WorkflowUpdateRecordBody that was common between update and
upsert and put back content into update
- creates WorkflowCreateRecordBody that is now common between create and
upsert
- simplifies shouldDisplayFormField
Before - using fields to update as update record action
<img width="546" height="823" alt="Capture d’écran 2025-10-30 à 10 00
24"
src="https://github.com/user-attachments/assets/9206bc8b-75c2-40fa-a8de-e708b6b2cd05"
/>
After - displaying all fields as create record action
<img width="546" height="823" alt="Capture d’écran 2025-10-30 à 10 00
04"
src="https://github.com/user-attachments/assets/87141a47-946f-4604-be55-f4c21ff4a3d8"
/>
When using primitive types such as array, number and boolean, we display
a text field in filters because fieldmetadataId is empty. We should
instead support these as we would do for our own fields.
Adding also a fix for https://github.com/twentyhq/twenty/issues/15282
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
This PR implements Sentry's AI agent monitoring by:
- Configuring vercelAIIntegration with recordInputs and recordOutputs
options
- Adding sendDefaultPii to Sentry.init() for better debugging
- Creating a shared AI_TELEMETRY_CONFIG constant to DRY up the telemetry
configuration
- Adding experimental_telemetry to all AI SDK calls (generateText,
generateObject, streamText)
All AI operations are now fully monitored in Sentry with complete
input/output recording for debugging and performance analysis.
Note: Currently on Sentry v9.26.0, which is compatible with this
implementation. No breaking changes from the v9-to-v10 migration guide
were found in the codebase.
Adds intelligent routing system that automatically selects the best
agent for user queries based on conversation context.
### Changes:
- Added `routerModel` column to workspace table for configurable router
LLM selection
- Implemented `RouterService` with conversation history analysis and
agent matching logic
- Created router settings UI in AI Settings page with model dropdown
- Removed agent-specific thread associations - threads are now
agent-agnostic
- Added real-time routing status notification in chat UI with shimmer
effect
- Removed automatic default assistant agent creation
- Renamed GraphQL operations from agent-specific to generic (e.g.,
`agentChatThreads` → `chatThreads`)
---------
Co-authored-by: Félix Malfait <felix.malfait@gmail.com>
Co-authored-by: Félix Malfait <felix@twenty.com>
Implements permission intersection (AND logic) to prevent permission
escalation when agents act on behalf of users.
### Changes:
- **Permission Intersection**: Operations requiring both user AND agent
permissions
- **RoleContext Type**: Unified type supporting single `roleId` or
multiple `roleIds` for intersection
- **CRUD Services**: Updated to accept `roleContext` for granular
permission control
- **Agent Integration**: Chat agents now use user + agent role
intersection for all operations
- **ORM Layer**: Enhanced `getRepository` to support multi-role
permission checks
### Related:
- Part 2 of ["Acting on behalf of user" concept
PR](https://github.com/twentyhq/twenty/pull/15103)
[Closes#1661](https://github.com/twentyhq/core-team-issues/issues/1661)
---------
Co-authored-by: Félix Malfait <felix.malfait@gmail.com>
## Summary
**Step 1 of 2:** Implements the "acting on behalf of user" concept for
workflows and agents to prevent permission escalation and maintain
proper audit trails.
## Problem
Previously, workflows and agents would bypass permissions regardless of
who initiated them, allowing users to escalate their privileges by
triggering workflows that performed actions they couldn't do directly.
## Solution
### For Workflows
Introduced `WorkflowExecutionContext` service that determines execution
mode:
- **Manual triggers/test button**: Uses user's roleId for permissions,
user's identity for `createdBy`
- **Automated triggers** (cron, database events, webhooks): Bypasses
permissions, uses workflow identity
### For Agents
**In Chat:**
- Always act on behalf of the user
- Use user's roleId for permission checks
- Use user's identity for `createdBy`
# Step 1 vs Step 2
### ✅ Step 1 (This PR): Acting on Behalf Concept
- Introduced `isActingOnBehalfOfUser` boolean concept
- Single roleId used for permission checks (user's OR system bypass)
- `createdBy` field properly attributes actions to initiator
- Prevents permission escalation in user-initiated flows
### 🔜 Step 2 (Future): Multi-Role Permission Support
- Support role intersection: `{ intersection: ['roleA', 'roleB'] }`
- Support role union: `{ union: ['roleA', 'roleB', 'roleC'] }`
- Enable user+agent collaboration scenarios
- Update `WorkspaceEntityManager` and `WorkspaceDatasource` to handle
multiple roleIds
---------
Co-authored-by: Félix Malfait <felix.malfait@gmail.com>
To avoid huge workflows to block the worker, we will enqueue a new job
every 20 steps.
This could be more than 20 if there are branches but I think this is
fine, the goal is only to have a limit set.
Also cleaning a bit the code to mark running steps as failed when
workflow fails.
I tested it on a huge workflow:
https://github.com/user-attachments/assets/d7b8e345-d1a1-4467-96fd-92117b500120
Filters should not cut the whole workflow. These should only stop the
branch. This PR:
- adds a new skipped status
- when a filter stops, it still goes to the next step
- the next step will execute if there is at least a successful step
- if only skipped step, it will be skipped as well
It allows to use filters in iterators.
https://github.com/user-attachments/assets/1cfca052-55c0-4ce5-9eb8-63736618d082
In this PR https://github.com/twentyhq/twenty/pull/14785 we deprecated
viewFilterOperand (from camelCase to capital snakeCase values), but we
had not migrated the existing workflow steps values. Hence they cannot
be executed!
Let's fix that by dealing with both operands, new and deprecated, to
mitigate the issue. Then we will add a command to migrate the values.
In the BE we have stored filter operand as IS_EMPTY, IS_NOT_EMPTY, etc.
For some reason in the FE we were manipulating IsEmpty, IsNotEmpty, etc.
(maybe because they were used before in Views before they were moved to
core)
So we were converting the operands in the FE from IS to Is
(convertViewFilterOperandFromCore) to read and manipulate viewFilters,
and then back to BE version to send mutations etc., from Is to IS
(convertViewFilterOperandToCore).
The migration is now over, so we can remove and simplify that code.
(In the 1-5:migrate-views-to-core command we still do the migration from
Is format to IS format.)
When all items have been processed, iterator should:
- store last iteration in history
- return the final result saying iteration have been successful. So it
gets stored by the executor
It was not doing the first step, so we were losing the last iteration.
Splitting into separated function + adding tests
- before reseting the step, enrich info with previous result
- reset only when a step is executed more than once. This will avoid to
store the initial `NOT_STARTED` status of the step
Félix Malfait