80cf187ab32d40fa468e24b1dcfde754ea1f28ff
28 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Félix Malfait
|
087bee0036 |
fix(ai): notify all tabs when a pending question is answered (#22491)
## Rationale `resolvePendingQuestion` updates the question tool-part to `answered` and re-claims the thread — but publishes **nothing**. The answering tab converges via a local browser event; every other tab keeps rendering the question card as interactive until the resumed stream's first chunk happens to arrive. A second tab (or teammate view on shared context) can attempt to answer an already-answered question and hit a confusing `QUESTION_NOT_PENDING` error. ## Why this is the root cause, not a symptom patch Answering a question is a state transition every subscriber cares about — exactly like queue promotion, message persistence, and stream errors, all of which publish. This transition just never did. The fix publishes the existing refetch-trigger event (`queue-updated`, which every tab already handles by refetching messages + thread state) right after resolution — no new event type, no new client code path, consistent by construction with how every other transition converges tabs. A dedicated `question-answered` event carrying the answers would save one refetch round-trip; the audit's verdict was that's over-engineering for a rare interaction. Publishing *before* the resume-enqueue is deliberate: even if the enqueue fails, the question **is** answered server-side, and tabs should reflect server truth. ## User impact Second tabs stop offering an interactive question that will error when submitted; everyone sees the answered state within a refetch instead of whenever the stream resumes. ## Test plan - [ ] CI green - [ ] Manual: two tabs on one thread, answer the question in tab A → tab B's card flips to answered without interaction https://claude.ai/code/session_01Lyi6zTema2FMVVh8MD6c38 --- _Generated by [Claude Code](https://claude.ai/code/session_01Lyi6zTema2FMVVh8MD6c38)_ <!-- This is an auto-generated description by cubic. --> <a href="https://cubic.dev/pr/twentyhq/twenty/pull/22491?utm_source=github" target="_blank" rel="noopener noreferrer" data-no-image-dialog="true"><picture><source media="(prefers-color-scheme: dark)" srcset="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"><source media="(prefers-color-scheme: light)" srcset="https://www.cubic.dev/buttons/review-in-cubic-light.svg"><img alt="Review in cubic" src="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"></picture></a> <!-- End of auto-generated description by cubic. --> |
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Félix Malfait
|
4aaf171d63 |
feat(ai): add ask_questions interactive clarifying-question tool (#22346)
## What & why Adds an `ask_questions` tool that lets the in-app **Ask AI** assistant **pause a turn to ask the user one or more multiple-choice questions** (per the [Figma design](https://www.figma.com/design/xt8O9mFeLl46C5InWwoMrN/Twenty?node-id=105959-117153)) and resume once answered — instead of guessing on ambiguous/consequential decisions. The tool is **harness-only**: an interactive question UI is meaningless without a user to answer it, so it must be absent from MCP and from head-less workflow agents. ## Design — true tool-result resume (not a synthetic user message) The user's answer is a **structured tool result bound to the `toolCallId`**, and the **same agent turn resumes** — exactly how Anthropic (`tool_result` by `tool_use_id`) and OpenAI (`function_call_output`) model human-in-the-loop. The naive form of this (leave the tool call in `input-available` to mean "pending") is **impossible** here: `finalizeDanglingToolParts` rewrites `input-available` → `output-error` ("Tool execution was interrupted") on both the persist path (`addMessage`) and the model-reload path (`chat-execution.service.ts`). That util is a load-bearing safety net, so weakening it is the wrong move. Instead: - `ask_questions` is an **inline, chat-only tool with an `execute` that returns a `status: 'pending'` result immediately**, so the tool part is always `output-available` and **immune to `finalizeDanglingToolParts`**. `stopWhen(hasToolCall('ask_questions'))` halts the turn right after the call (the model never sees the placeholder). - A nullable **`thread.pendingQuestionMessageId`** marker records that a turn is awaiting an answer. - The new **`answerAgentChatQuestion`** mutation atomically *claims* the question (clears the marker, marks the thread streaming), **writes the answer onto the same tool part** (`status: 'answered'`), and **re-enqueues the turn via the existing `existingTurnId` plumbing** (`isResume` bypasses the per-turn dedup guard). On resume `finalizeDanglingToolParts` leaves the `output-available` part untouched and `convertToModelMessages` emits `assistant(tool_use)` + `tool_result(answers)`, so the model continues. This achieves the platform-aligned semantics **without** weakening the finalize safety net or inventing a fragile new part state. ### Meets the two requirements - **Survives refresh, scoped per-thread** — the pending state is a normal persisted `output-available` part + the thread marker; the frontend card is derived per-thread from the loaded messages, so it re-appears on reload and only on its own thread. - **Takes priority over the queue** — a unified `isBlocked = activeStreamId || pendingQuestionMessageId` gate is applied in both `sendChatMessage` (new messages queue) and `flushNextQueuedMessage` (the drain). The queue cannot unpile until the question is answered and the resumed turn completes. ### Harness-only by construction `ask_questions` is added **only** to the chat's inline `activeTools` (like `learn_tools`/`execute_tool`/`load_skills`). It never enters the tool registry/catalog, so it is invisible to MCP and to workflow agents — no `MCP_EXCLUDED_TOOL_NAMES` entry needed. ## UX While a question is pending, the **composer is replaced by the question card** (matching the Figma): question title + pager (`1/2`), numbered option rows (`IconSquareNumber*`) with per-option info-icon descriptions and a "Recommended" badge, and the normal composer as the free-text fallback ("Type anything to do differently."). The transcript shows a compact "Asking questions…" status line that becomes an answered summary. ## Changes **twenty-shared** - `ai/types/AskQuestionsToolTypes.ts` — `AskQuestionItem/Option/Answer/Result`, `ASK_QUESTIONS_TOOL_NAME`. **twenty-server** - `ai-chat/tools/ask-questions.tool.ts` — inline tool factory (pending-result `execute`, zod schema, 1–4 questions × 2–4 options). - `chat-execution.service.ts` — add to `activeTools` + `preloadedToolNames`; `hasToolCall` in `stopWhen`. - `chat-system-prompts.const.ts` — when-to-use guidance. - `entities/agent-chat-thread.entity.ts` — `pendingQuestionMessageId` column. - `stream-agent-chat.job.ts` — set the marker on a question pause; bypass the dedup guard on resume; suppress the no-text warning for question pauses. - `agent-chat-streaming.service.ts` — gate `flushNextQueuedMessage`; `enqueueResumeStream`. - `agent-chat.resolver.ts` — gate `sendChatMessage`; `answerAgentChatQuestion` mutation. - `agent-chat.service.ts` — `resolvePendingQuestion` (atomic claim + write answer). - `dtos/agent-chat-question-answer.input.ts`, `ai.exception.ts` (`QUESTION_NOT_PENDING`), `utils/find-pending-question-part.util.ts`. **twenty-front** - `components/AiChatQuestionCard.tsx` — the interactive card (matches Figma tokens) + `__stories__/AiChatQuestionCard.stories.tsx`. - `components/AiChatEditorSection.tsx` — swap the composer for the card while pending. - `components/AiChatQuestionStatusRenderer.tsx` + branch in `AiChatAssistantMessageRenderer.tsx`. - `states/selectors/agentChatPendingQuestionComponentSelector.ts`, `types/AgentChatPendingQuestion.ts`. - `hooks/useSubmitQuestionAnswer.ts` + `utils/markQuestionAnswered.ts` (optimistic) + `graphql/mutations/answerAgentChatQuestion.ts`. A design doc lives at `packages/twenty-server/docs/ASK_USER_QUESTION_TOOL_PLAN.md`. ## Migration Adds a nullable `pendingQuestionMessageId` (uuid) column to `core.agentChatThread`. Needs a generated **fast instance command** (`database:migrate:generate --name addThreadPendingQuestion --type fast`) — see "Verification status". ## Tests - Server: `ask-questions.tool.spec.ts` (pending echo + schema bounds), `find-pending-question-part.util.spec.ts`. - Front: `markQuestionAnswered.test.ts`, plus the Storybook story. ## Verification status (please read) This branch was authored in an environment where the monorepo `yarn install` repeatedly failed on transient TLS resets from the package registry, so I could **not** locally run the mechanical gates. The logic was reviewed by hand and the `ai@6.0.97` exports used (`hasToolCall`, `stepCountIs`, `generateId`) were confirmed against the package's type defs. Still **TODO** (will rely on CI / a follow-up once deps install): - [ ] `nx run twenty-shared:generateBarrels` (the `ai/index.ts` export was added by hand; regen to reconcile) - [ ] `nx run twenty-front:graphql:generate` (new mutation + input type) - [ ] generate the fast instance command (migration) for the new column - [ ] `typecheck` + `lint:diff-with-main` (front + server) — expect minor import-ordering autofixes - [ ] run the unit tests **Screenshots:** reproducing the live flow needs an AI provider API key (to get the model to actually call `ask_questions`), which isn't available here. The card can be screenshotted from its **Storybook story** (`AiChatQuestionCard.stories.tsx`) with no API key — I'll add that image once deps install, or a reviewer can run `nx storybook twenty-front`. 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_01AArS8H3y3Z1Qwm763xhPLB --- _Generated by [Claude Code](https://claude.ai/code/session_01AArS8H3y3Z1Qwm763xhPLB)_ <!-- This is an auto-generated description by cubic. --> <a href="https://cubic.dev/pr/twentyhq/twenty/pull/22346?utm_source=github" target="_blank" rel="noopener noreferrer" data-no-image-dialog="true"><picture><source media="(prefers-color-scheme: dark)" srcset="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"><source media="(prefers-color-scheme: light)" srcset="https://www.cubic.dev/buttons/review-in-cubic-light.svg"><img alt="Review in cubic" src="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"></picture></a> <!-- End of auto-generated description by cubic. --> |
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Félix Malfait
|
da6a2ee300 |
fix(ai-chat): keep streams alive on silent SSE death + make the stream job idempotent (#22201)
## Problem In production, an AI-chat assistant response sometimes freezes mid-stream (partial text, looks hung), then "picks up again on its own" later without the user resending and without a known worker restart. Root cause: the **agent-chat SSE subscription has no keepalive and no silent-death detection**. - Delivery is fire-and-forget Redis pub/sub (`SubscriptionService.publishToAgentChat`) and the resolver returns the **raw** iterator — unlike `EventStreamResolver`, which heartbeats every 30s via `wrapAsyncIteratorWithLifecycle`. - During a quiet model/tool gap the connection sends no bytes, so a proxy/LB/NAT can silently drop it mid-stream. `graphql-sse` neither surfaces an error nor resumes with `Last-Event-ID`, and **nothing re-pulls the existing Redis chunk catch-up on reconnect** (it only runs on thread (re)mount / `message-persisted` refetch). - So the live view freezes; recovery only happens when the terminal `message-persisted` fires a full refetch from the DB — the observed "self-recovery". This is the **same silent-SSE-death class fixed for the DB event stream in #21061**, which was never applied to the agent-chat path. The symptom also matches #21096 (worker logs the job finishing, client never updates, reload shows the message). It is **not** queue prioritization, and it is **not** addressed by #22193 (which only stabilizes the assistant message id and removes end-of-stream flicker). A secondary, independent self-recovery path also existed: BullMQ stalled-job re-run (default 30s `lockDuration`, no idempotency guard) re-streaming the whole turn → duplicate assistant messages / double billing. ## Changes ### Commit 1 — keepalive + silent-death recovery (ports the #21061 pattern to agent chat) - **Shared:** new `keepalive` variant on `AgentChatSubscriptionEvent`. - **Server:** wrap the agent-chat subscription iterator with `wrapAsyncIteratorWithLifecycle` — emit a `keepalive` on connect and every `APPLICATION_KEEPALIVE_INTERVAL_MS` (30s) so the connection keeps flushing bytes and a dead connection becomes detectable. - **Client:** track the last received event timestamp (refreshed on every chunk/keepalive in the SSE `next` sink); new `AgentChatStreamKeepAliveEffect` forces a resubscribe + messages refetch after 90s of silence, so the durable Redis chunk list backfills the gap (`firstLiveSeq` is reset on resubscribe). ### Commit 2 — stream-job idempotency + lockDuration - Thread a `lockDuration` option through `MessageQueueWorkerOptions` + the BullMQ driver; set `aiStreamQueue` to 10 min so long streams aren't falsely stalled. - Guard `StreamAgentChatJob.handle` with a `streamId`-scoped Redis lock (`SET NX PX` + compare-and-delete release) so a stalled re-run is skipped instead of double-processing. ## Verification ⚠️ I could **not run typecheck/lint locally** — `yarn install` could not complete in this environment (transient registry network aborts before the link step, so `node_modules` never populated). **Please rely on CI for type/lint verification.** The changes are written to match existing conventions; the points most worth a reviewer's eye are the resolver's iterator typing and the ioredis `set(..., 'PX', ttl, 'NX')` overload. How to confirm the root cause in prod: a frozen client with the worker logging `StreamAgentChatJob processed in …ms` and no `[AI_CHAT_NO_TEXT]` is the silent-death signature (check reverse-proxy idle/buffering). For the secondary path, watch `aiStreamQueue` `stalled`/re-processed metrics and duplicate turns around worker restarts. ## Notes / trade-offs - The 10-min `lockDuration` means a genuinely crashed worker's job isn't reclaimed for up to 10 min; the client-side keepalive/catch-up recovers the view independently, and the idempotency lock prevents duplicates. Faster dead-worker recovery could be a follow-up. - Touches `useAgentChatSubscription.ts` / `AgentChatRuntimeEffects.tsx` / `stream-agent-chat.job.ts`, which #22193 also touches — trivial rebase expected. Opened as **draft** pending CI. https://claude.ai/code/session_018dF82A1VcsuWMxPLmdY3dm --- _Generated by [Claude Code](https://claude.ai/code/session_018dF82A1VcsuWMxPLmdY3dm)_ <!-- This is an auto-generated description by cubic. --> <a href="https://cubic.dev/pr/twentyhq/twenty/pull/22201?utm_source=github" target="_blank" rel="noopener noreferrer" data-no-image-dialog="true"><picture><source media="(prefers-color-scheme: dark)" srcset="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"><source media="(prefers-color-scheme: light)" srcset="https://www.cubic.dev/buttons/review-in-cubic-light.svg"><img alt="Review in cubic" src="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"></picture></a> <!-- End of auto-generated description by cubic. --> |
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|
Etienne
|
5ca41d55fb |
feat(ai): humanize tool-call (#21976)
# Humanize tool-call labels cc: https://github.com/twentyhq/twenty/pull/21462 ## Preview <img width="459" height="156" alt="Screenshot 2026-06-22 at 19 13 11" src="https://github.com/user-attachments/assets/e7a2f5f5-cd09-4ec6-920b-5eb16b98285c" /> <img width="461" height="156" alt="Screenshot 2026-06-22 at 19 14 54" src="https://github.com/user-attachments/assets/c2114d2e-2aa8-499a-9801-68e3bb7c45f8" /> <img width="461" height="505" alt="Screenshot 2026-06-22 at 19 15 01" src="https://github.com/user-attachments/assets/ee9ca5d0-8e79-4c63-a2ff-ed5e359a9a9c" /> ## Why In the AI chat, tool steps were displayed using raw tool identifiers (`find_many_companies`, `create_one_task`, `send_email`...) and labels were partially reconstructed/humanized on the frontend. This was hard to localize and inconsistent across tool categories. This PR makes the **backend the single source of truth for human-readable, localized tool labels**, exposes them through `getToolIndex`, and reduces the frontend to a thin resolver that picks the right label for the current status (in-progress / completed). ## What changed ### Backend - `ToolIndexEntry` (and the `getToolIndex` GraphQL DTO) now carry `label`, `inProgressLabel?`, `completedLabel?`. - New `getCrudToolLabels(operation, objectLabel, i18nService, locale)` builds CRUD labels from a verb table (Search / Find / Group / Create / Update / Upsert / Delete × imperative / in-progress / completed) + the (translated, lowercased) object label. - New `translate-tool-label.util.ts` translates a source label via `I18nService` (`generateMessageId` → fallback to source when no translation exists). - Action tools: labels extracted to the `ACTION_TOOL_LABELS` constant (`msg` + `i18nLabel`) and translated in `ActionToolProvider.buildDescriptor`. - Logic-function tools use the function name as label; `toolSetToDescriptors` (workflow / view / metadata / dashboard) accepts an optional `labels` map and falls back to a humanized tool name. - Labels are localized server-side using the request locale (`@RequestLocale` → `buildToolIndex` → `context.locale`, threaded through `ToolContext` / `ToolProviderContext`). - `code_interpreter` schema now asks the model for `loadingMessage` (present tense) and `completedMessage` (past tense), so its status text is model-generated. - Removed the old generic `loadingMessage` injection mechanism (`wrap-tool-for-execution.util.ts` deleted; `wrapJsonSchemaForExecution` / `stripLoadingMessage` no longer wrap every tool). ### Frontend - New `useToolLabelMap()` hook builds a `Map<name, { label, inProgressLabel, completedLabel }>` from `getToolIndex`. - `getToolDisplayMessage` → `resolveToolDisplayMessage({ input, toolName, isFinished, labelMap, output })`: a small resolver registry keyed by tool name (`execute_tool`, `web_search`, `learn_tools`, `load_skills`, `code_interpreter`, default). - Default resolver prefers backend `completedLabel` / `inProgressLabel`, falling back to `Ran X` / `Running X`. - `learn_tools` / `load_skills` resolve their inner tool/skill names to labels (label map → tool output labels via `getToolOutputLabelEntries` → raw name). - `code_interpreter` step is now expandable to show the code even while running. ## How tool labelling flows (BE → FE) ```text BACKEND ┌───────────────────────────────────────────────────────────────────────────┐ │ Tool providers (per category) → ToolIndexEntry │ │ │ │ DatabaseToolProvider │ │ getCrudToolLabels(operation, object.labelPlural/Singular, i18n, locale) │ │ verb table (Search/Create/Update/Delete…) + translateToolLabel(object) │ │ → { label, inProgressLabel, completedLabel } │ │ │ │ ActionToolProvider │ │ ACTION_TOOL_LABELS[toolId] (msg) → translateToolLabel(…, locale) │ │ → { label, inProgressLabel?, completedLabel? } │ │ │ │ LogicFunctionToolProvider → label = logicFunction.name │ │ toolSetToDescriptors → label = labels[name] ?? humanize(name) │ │ (workflow / view / metadata / dashboard) │ └───────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌───────────────────────────────────────────────────────────────────────────┐ │ GraphQL Query getToolIndex : [ToolIndexEntry] │ │ { name, label, inProgressLabel, completedLabel, description, │ │ category, objectName, icon } │ └───────────────────────────────────────────────────────────────────────────┘ │ ▼ FRONTEND ─ resolve the right label for the current status ┌───────────────────────────────────────────────────────────────────────────┐ │ useGetToolIndex() → useToolLabelMap() │ │ Map<name, { label, inProgressLabel?, completedLabel? }> │ └───────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌───────────────────────────────────────────────────────────────────────────┐ │ resolveToolDisplayMessage({ input, toolName, isFinished, labelMap, output })│ │ │ │ TOOL_LABEL_RESOLVERS[toolName] ?? defaultResolver │ │ ├─ execute_tool → unwrap { toolName, arguments } then re-resolve │ │ ├─ web_search → "Searching/Searched the web for <query>" │ │ ├─ learn_tools → "Learning/Learned <labels>" │ │ ├─ load_skills → "Loading/Loaded <labels>" │ │ │ inner names resolved via: labelMap → output labels → raw name │ │ ├─ code_interpreter → model's loadingMessage / completedMessage │ │ └─ default → isFinished │ │ ? completedLabel ?? "Ran <label>" │ │ : inProgressLabel ?? "Running <label>" │ └───────────────────────────────────────────────────────────────────────────┘ │ ▼ Rendered by ThinkingStepsDisplay / ToolStepRenderer ``` ## Localization notes - Standard object labels and action/CRUD verbs are translated server-side via `I18nService` using the requester's locale. - Custom object labels are not translated unless a workspace custom translation exists (matched by `generateMessageId`); otherwise the source label is used as-is. ## Tests - **FE:** `resolveToolDisplayMessage` / `getToolOutputLabelEntries` (status selection, inner-name resolution, `code_interpreter` model labels, fallbacks). - **BE:** `toolSetToDescriptors` (label map + humanized fallback) and `database-tool.provider` label generation. <!-- This is an auto-generated description by cubic. --> <a href="https://cubic.dev/pr/twentyhq/twenty/pull/21976?utm_source=github" target="_blank" rel="noopener noreferrer" data-no-image-dialog="true"><picture><source media="(prefers-color-scheme: dark)" srcset="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"><source media="(prefers-color-scheme: light)" srcset="https://www.cubic.dev/buttons/review-in-cubic-light.svg"><img alt="Review in cubic" src="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"></picture></a> <!-- End of auto-generated description by cubic. --> |
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Charles Bochet
|
0064ff6741 |
fix(ai): validate AI agent output field names against schema-key constraint (#21834)
## Problem
On a self-hosted instance, an AI Agent workflow action fails at run time
with an opaque model error:
```
The model returned the following errors: tools.0.custom.input_schema.properties:
Property keys should match pattern '^[a-zA-Z0-9_.-]{1,64}$'
```
This is Anthropic's validation on tool `input_schema` **property keys**.
An AI Agent's structured **Output** fields are turned into a JSON schema
and passed to the model as a tool; each output **variable name** becomes
a property key. Anthropic rejects any key that does not match
`^[a-zA-Z0-9_.-]{1,64}$` — most commonly a name containing a **space**
(e.g. `meetings brief`), but also names over 64 characters or with other
symbols.
Until now nothing validated this: `fieldsToSchema` writes
`properties[field.name]` verbatim, so a bad name only failed once the
workflow executed, with an error that gives the user no idea what to
fix. It doesn't reproduce on every instance — it depends purely on how
the workflow's output variables happen to be named.
## Fix
Introduce a single shared check,
`isValidAgentResponseSchemaPropertyKey`, and enforce it in two places:
- **Backend** — `validateAgentResponseFormat` now rejects invalid output
field names at agent **save time** with a clear `userFriendlyMessage`,
instead of letting the broken schema reach the model. This also gates
agents created via the API and re-saves of existing bad data.
- **Frontend** — the output schema builder shows an inline error on the
Variable Name field as soon as an invalid name is entered.
## Tests
- Unit test for the shared validity check (valid + invalid cases:
spaces, leading space, empty, > 64 chars, symbols, unicode).
- Unit test for `validateAgentResponseFormat` covering text/json
formats, valid names, a space in a name, an over-length name, and
reporting multiple invalid names at once.
## Notes for the reporter
The immediate unblock for an affected workflow is to rename the output
variable to remove the space (e.g. `meetings brief` → `meetings_brief`)
and retry the run. With this change the bad name is caught up front with
an explanation rather than failing mid-run.
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|
martmull
|
c2ca90c255 |
feat(sdk): add runAgent() to run app agents from logic functions (#21157)
<img width="948" height="593" alt="image" src="https://github.com/user-attachments/assets/d990fa98-3cfd-469d-ab7f-0b2d4ccf3afc" /> <img width="1361" height="802" alt="image" src="https://github.com/user-attachments/assets/1091f598-49f3-4c16-92ea-1e1c200181e2" /> ## Add `runAgent()` to the Logic Function SDK Lets an app's logic function run one of its own AI agents server-side and get the result back synchronously — reusing the existing agent executor instead of a new bespoke transport. ### Backend - New **`runAgent` GraphQL mutation** (metadata schema) in `ai-agent-execution`, wrapping the existing `AgentAsyncExecutorService.executeAgent`. Scopes the agent lookup to the calling application and runs it under an application auth context. - New `@AuthApplication()` param decorator (mirrors `@AuthWorkspace()`) — first GraphQL resolver authenticated by an **application access token**. - Guarded by `WorkspaceAuthGuard` + `SettingsPermissionGuard(PermissionFlagType.AI)`: the app's role must grant the `AI` permission flag. ### SDK - `runAgent({ agentUniversalIdentifier, prompt })` posts the mutation to `/metadata` with the app token via a new runtime GraphQL transport. Returns `{ result, hasNoMoreAvailableCredits }`. - Refactored the connections helpers onto a shared `postAppEndpoint` util (removes duplicated transport logic). ### Frontend - App install permission modal now shows an explicit consent line — _"Run AI agents and bill AI credits to your workspace"_ — when the app's role requests the `AI` flag. ### Docs - Documented `runAgent` and its `AI` permission-flag requirement in _Skills & Agents_. - Fixed outdated role-permission examples in _Roles & Permissions_ (`permissionFlags` → `permissionFlagUniversalIdentifiers`, `PermissionFlag` → `SystemPermissionFlag`). ### Test plan - [x] SDK unit tests (`run-agent.spec.ts`) — request shape, GraphQL/HTTP error handling, missing env vars - [x] `twenty-server`, `twenty-front`, `twenty-shared` typecheck + lint - [ ] Manual: install an app granting the `AI` flag, call `runAgent()` from a logic function, confirm the agent runs and credits are billed --------- Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com> |
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Félix Malfait
|
de044f4b45 |
feat(ai-chat): add navigation menu item + webhook tool providers (#20759)
## Summary
Exposes two Twenty primitives to the AI chat that it could not
previously manage:
- **Navigation menu items** — workspace nav and personal favorites
(favorites are just nav items with `scope: 'user'`).
- **Webhooks** — full CRUD with a structured operations input (record +
metadata events).
Page layouts and workflow runs were originally in this PR but have been
split out — they touch heavier surfaces (21 widget configurations and
the workflow runner cycle, respectively) and deserve their own focused
PRs.
### Tool inventory (8 new tools across 2 providers)
| Provider | Tools |
|---|---|
| NavigationMenuItem | `list_`, `create_`, `update_`,
`delete_navigation_menu_item` |
| Webhook | `list_`, `create_`, `update_`, `delete_webhook` |
### Design notes
- Both providers follow the established **view-style pattern**: tool
workspace service lives in the entity module's `tools/` folder, is
provided + exported by the entity module, and `ToolProviderModule`
imports the entity module. No `@Global()` modules or injection tokens
introduced.
- `create_navigation_menu_item` uses a Zod `discriminatedUnion` on
`type` (`FOLDER` / `LINK` / `OBJECT` / `VIEW` / `RECORD` /
`PAGE_LAYOUT`). `scope: 'workspace' | 'user'` switches between shared
nav and personal favorites — the underlying
`NavigationMenuItemAccessService` enforces LAYOUTS for workspace writes.
- Webhook operations accept both record events (`{kind:'record', object,
event}` → `<object>.<event>`) and metadata events (`{kind:'metadata',
metadataName, operation}` → `metadata.<metadataName>.<operation>`).
- Permissions reuse existing flags (`LAYOUTS`, `API_KEYS_AND_WEBHOOKS`).
No new permission flags, no migrations.
### Category cleanup
- New: `ToolCategory.NAVIGATION_MENU_ITEM`, `ToolCategory.WEBHOOK`.
- `ToolCategory.VIEW_FIELD` → folded into `VIEW`. Same permission gate,
same domain — separate category was organizational drift.
- `navigate_app` action stays in `ToolCategory.ACTION` where it belongs.
### System prompt addition
[chat-system-prompts.const.ts](packages/twenty-server/src/engine/metadata-modules/ai/ai-chat/constants/chat-system-prompts.const.ts)
now teaches the AI:
- Favorites are nav items with `scope: 'user'`.
- A default OBJECT nav item is auto-created with
`create_object_metadata` — don't double-create.
### One file = one export
Every new schema / type / util file has exactly one top-level export.
## Test plan
- [ ] `npx nx typecheck twenty-server` — passes
- [ ] Spin up locally and exercise via AI chat:
- [ ] "Pin the Companies view to my favorites in a folder called
Important." → `create_navigation_menu_item` (FOLDER, user) then (VIEW,
user, folderId)
- [ ] "Register a webhook to https://example.com firing when any person
is created or updated." → `create_webhook` with discriminated operations
- [ ] Verify workspace-scoped nav writes are denied for a user without
LAYOUTS permission
- [ ] Verify user-scoped nav writes work without LAYOUTS permission
## Follow-ups (separate PRs)
- Page layout tools (record-page, record-index, standalone) — needs
widget-config strategy.
- Workflow run tools (list, get, run, stop) — uses the workflow-runner
cycle path.
- Dashboard / page-layout tool unification —
`DashboardToolWorkspaceService` and a future
`PageLayoutToolWorkspaceService` both inject the same trio
(PageLayout/Tab/Widget services).
- Webhook Settings page reads from raw Apollo query — switch to the
metadata store so it refreshes when the AI mutates webhooks.
|
||
|
Etienne
|
aecfe699f4 |
feat(ai-chat) - Stop ai thinking if credits exhausted (#20526)
Billing is now decremented per-step, not per-turn. The onStepFinish callback in chat-execution.service.ts calls a new decrementAndCheckAvailableCredits method on each model step, so Redis is debited incrementally as the agent runs rather than all at once at the end. Credit exhaustion stops the stream mid-run. When a step depletes the remaining credits, a hasNoMoreAvailableCredits flag is set and passed into the stopWhen predicate of streamText, causing the agent to halt before starting the next step. A new credits-exhausted event is introduced. After the stream drains and the response is persisted, if credits ran out the job publishes a dedicated credits-exhausted event to the frontend instead of the normal message-persisted event. The frontend handles this new event. useAgentChatSubscription has a new credits-exhausted case that sets a BILLING_CREDITS_EXHAUSTED-coded error on the atom, closes the writer, and stops the streaming state — triggering the existing AiChatCreditsExhaustedMessage UI. |
||
|
Etienne
|
91124a3cb8 |
AI - Add azure foundry provider (#20170)
[Merge this before](https://github.com/twentyhq/twenty-infra/pull/655) Co-authored-by: Félix Malfait <felix.malfait@gmail.com> |
||
|
nitin
|
6c1c0737b0 |
Clarify registry tools vs native model tool binding (#20022)
## Intent This is a small foundation cleanup for the tool architecture. The main decision is: registry tools and native SDK/model tools are different things. - Registry tools have descriptors, schemas, catalog entries, and execute through `ToolExecutorService` - Native model tools are opaque AI SDK objects, bound directly into the model `ToolSet` - Surfaces still own their policy: chat, MCP, and workflow agents decide what they expose ## What changed - Removed `NATIVE_MODEL` from `ToolCategory` - Kept `ToolRegistryService` focused on registry-backed tools only - Moved native model tool binding through `NativeToolBinderService` - Reused native binding from chat instead of duplicating provider-specific web-search logic - Kept MCP local execution exclusions in a dedicated constant - Moved surface-specific constants into dedicated constant files ## What comes next - Move hardcoded chat app preloads, like Exa web search, into app/manifest metadata - Decide a clearer policy for local runtime tools like code interpreter and HTTP request - Gradually document the three tool shapes: registry tools, native model tools, and local runtime tools --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Félix Malfait <FelixMalfait@users.noreply.github.com> Co-authored-by: Félix Malfait <felix.malfait@gmail.com> |
||
|
Félix Malfait
|
6117a1d6c0 |
refactor: standardize AI acronym to Ai (PascalCase) across internal identifiers (#19837)
## Summary
The "AI" acronym was rendered inconsistently across the codebase. The
backend AI module had settled on PascalCase `Ai` (`AiAgentModule`,
`AiBillingService`, `AiChatModule`, `AiModelRegistryService`, etc.),
while frontend components, several DTOs, a few types, and shared
identifiers still used all-caps `AI` (`AIChatTab`,
`AISystemPromptPreviewDTO`, `SettingsPath.AIPrompts`, ...). CLAUDE.md
specifies PascalCase for classes; this PR normalizes everything internal
to `Ai`.
**This is a pure internal rename.** The GraphQL schema is untouched —
`@ObjectType` decorator string arguments, resolver method names (which
become Query/Mutation field names), gql template contents, and the
`generated-metadata/graphql.ts` file are preserved verbatim. The only
visible change is TypeScript identifiers and file names.
## Also folded in (adjacent cleanups)
- **`AgentModelConfigService` → `AiModelConfigService`**. Lives in
`ai-models/` and is used by multiple AI code paths, not just the Agent
entity. The "Agent" prefix was misleading.
- **`generate-text-input.dto.ts` → `generate-text.input.ts`**. The
`ai-agent/dtos/` folder already uses `<entity>.input.ts` convention for
Input classes (`create-agent.input.ts` etc.); the old path mixed
`.dto.ts` file extension with a class that has no DTO suffix. File
rename only; class stays `GenerateTextInput`.
- **Removed stale TODO** in `ai-model-config.type.ts` that asked for the
`AiModelConfig` rename that this PR performs.
## Rename methodology
Bulk rename via perl with anchored regex
`(?<!['"])(?<![A-Z.])AI([A-Z])(?=[a-z])/Ai$1/g`:
- **Lookbehind for non-uppercase** skips adjacent acronyms (`MOSAIC`,
`OIDCSSO`) and leaves `AIRBNB_ID` alone.
- **Lookbehind for non-quote** protects most string literals.
- **Lookahead for lowercase** restricts matches to PascalCase
identifiers (`AIChatTab`), leaving SCREAMING_SNAKE constants untouched.
Strict file-scope exclusions: `generated-metadata/**`, `generated/**`,
`locales/**`, `migrations/**`, `illustrations/**`, `halftone/**`, and
the two gql template files (`queries/getAISystemPromptPreview.ts`,
`mutations/uploadAIChatFile.ts`).
Post-rename reverts for identifiers where the regex was too eager:
- Backend resolver method names kept: `getAISystemPromptPreview`,
`uploadAIChatFile` (they are GraphQL field names).
- `@ObjectType('AdminAIModels')` / `('AISystemPromptPreview')` /
`('AISystemPromptSection')` kept as-is.
- Backend classes `ClientAIModelConfig` / `AdminAIModelConfig` kept
as-is (they use `@ObjectType()` with no argument, so the class name IS
the schema name).
- External-library symbols restored: `OpenAIProvider`,
`createOpenAICompatible`, `vercelAIIntegration`.
File renames use a two-step rename to work on macOS case-insensitive
filesystems: `git mv X.tsx X.tsx.tmp && git mv X.tsx.tmp renamed.tsx`.
## Diff audit
- 0 changes to migrations
- 0 changes to locale `.po` / `.ts` files
- 0 changes to `generated-metadata/graphql.ts`
- 0 changes to website illustration files (base64 blobs preserved)
- 0 renames inside user-facing translation strings (`t\`…\``,
`msg\`…\``, `<Trans>…</Trans>`)
## Test plan
- [x] `npx nx typecheck twenty-server` — PASS
- [x] `npx nx typecheck twenty-front` — PASS
- [x] `npx jest ai-model admin agent-role` — 79/79 PASS
- [x] `npx oxlint --type-aware` on 118 changed files — 0 errors
- [x] `npx prettier --check` on 118 changed files — clean
- [ ] CI
|
||
|
Abdul Rahman
|
7f2b853ae1 | feat: add message compaction for AI chats (#19205) | ||
|
nitin
|
3c067f072c | [AI] Improve tools tab (#19221) | ||
|
Félix Malfait
|
fd7387928c |
feat: queue messages + replace AI SDK with GraphQL SSE subscription (#19203)
## Summary - **Queue messages while streaming**: Messages sent during active AI streaming are queued server-side and auto-flushed when the current stream completes. Frontend renders queued messages optimistically in a dedicated queue UI. - **Drop `@ai-sdk/react` + `resumable-stream`**: Replace the dual HTTP SSE + AI SDK client architecture with a single GraphQL SSE subscription per thread. All events (token chunks, message persistence, queue updates, errors) flow through Redis PubSub → GraphQL subscription. - **Server-driven architecture**: The server decides whether to queue or stream (via `POST /:threadId/message`). The frontend mirrors this decision for optimistic rendering but defers to the server response. - **Reuse AI SDK accumulation logic**: `readUIMessageStream` from the `ai` package handles chunk-to-message accumulation on the frontend, avoiding a custom 780-line accumulator. ## Key files **Backend:** - `agent-chat-event-publisher.service.ts` — publishes events to Redis PubSub - `agent-chat-subscription.resolver.ts` — GraphQL subscription resolver - `stream-agent-chat.job.ts` — publishes chunks via PubSub instead of resumable-stream - `agent-chat.controller.ts` — unified `POST /:threadId/message` endpoint **Frontend:** - `useAgentChatSubscription.ts` — subscribes to `onAgentChatEvent`, bridges to `readUIMessageStream` - `useAgentChat.ts` — send/stop/optimistic rendering (no more AI SDK) - `AgentChatStreamSubscriptionEffect.tsx` — replaces `AgentChatAiSdkStreamEffect.tsx` ## Test plan - [ ] Send message on new thread → optimistic render, streaming response appears - [ ] Send message while streaming → queued instantly (no flash in main thread) - [ ] Queued message auto-flushes after current stream completes - [ ] Remove queued message via queue UI - [ ] Stop streaming mid-response - [ ] Leave chat idle for several minutes → streaming still works after (SSE client recycling) - [ ] Token refresh during session → requests succeed (authenticated fetch) - [ ] Switch threads while streaming → clean subscription handoff Made with [Cursor](https://cursor.com) --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
||
|
Félix Malfait
|
908aefe7c1 |
feat: replace hardcoded AI model constants with JSON seed catalog (#18818)
## Summary - Replaces per-provider TypeScript constant files (`openai-models.const.ts`, `anthropic-models.const.ts`, etc.) with a single `ai-providers.json` catalog as the source of truth - Adds runtime model discovery via AI SDK for self-hosted providers, with `models.dev` enrichment for pricing/capabilities - Introduces composite model IDs (`provider/modelId`) for canonical, conflict-free identification - Simplifies provider configuration: API keys are injected from environment variables (e.g., `OPENAI_API_KEY`) - Adds admin panel UI for provider management (add/remove/test), model discovery, recommended model configuration, and default fast/smart model selection per workspace - Removes deprecated config variables (`AI_DISABLED_MODEL_IDS`, `AUTO_ENABLE_NEW_AI_MODELS`, etc.) - Adds database migration for composite model ID format ## Test plan - [ ] Server typecheck passes - [ ] Frontend typecheck passes - [ ] Server unit tests pass - [ ] Frontend unit tests pass - [ ] CI pipeline green - [ ] Admin panel AI tab loads correctly - [ ] Provider discovery works for configured providers - [ ] Model recommendation toggles persist - [ ] Default fast/smart model selection works Made with [Cursor](https://cursor.com) |
||
|
Lucas Bordeau
|
cb3e32df86 |
Fix AI demo workspace skill (#18575)
This PR fixes what allows to have a working demo workspace skill. - Skill updated many times into something that works - Fixed infinite loop in AI chat by memoizing ai-sdk output - Finished navigateToView implementation - Increased MAX_STEPS to 300 so the chat don't quit in the middle of a long running skill - Added CreateManyRelationFields |
||
|
Lucas Bordeau
|
2a82df7073 |
AI tools to create a demo workspace (#18236)
This PR adds the necessary tool to create a demo workspace with : relevant custom objects and fields, mock data and a real dashboard with graph widgets. It is still a bit under-optimized and slow but it works. This PR also adds an AI tool that allows to see what happens in real time, it navigates the app and waits when necessary. --------- Co-authored-by: Félix Malfait <felix.malfait@gmail.com> |
||
|
Etienne
|
26f0a416a1 |
File storage cleaning (#18381)
- Remove feature flag - Remove legacy methods in file-upload and file-service - Migrate AI Chat to new file management --------- Co-authored-by: Charles Bochet <charles@twenty.com> |
||
|
Abdullah.
|
09e1684300 |
feat: show auto-generated conversation title for AI chat. (#17922)
## Summary Replaces the static "Ask AI" header in the command menu with the conversation’s auto-generated title once it’s set after the first message. ## Changes - **Backend:** Title is generated after the first user message (existing behavior). - **Frontend:** After the first stream completes, we fetch the thread title and sync it to: - `currentAIChatThreadTitleState` (persists across command menu close/reopen) - Command menu page info and navigation stack (so the title survives back navigation) - **Entry points:** Opening Ask AI from the left nav or command center uses the same title resolution (explicit `pageTitle` → current thread title → "Ask AI" fallback). - **Race fix:** Title sync only runs when the thread that finished streaming is still the active thread, so switching threads mid-stream doesn’t overwrite the current thread’s title. --------- Co-authored-by: Félix Malfait <felix@twenty.com> Co-authored-by: Cursor <cursoragent@cursor.com> |
||
|
Félix Malfait
|
3216b634a3 |
feat: improve AI chat - system prompt, tool output, context window display (#17769)
⚠️ **AI-generated PR — not ready for review** ⚠️ cc @FelixMalfait --- ## Changes ### System prompt improvements - Explicit skill-before-tools workflow to prevent the model from calling tools without loading the matching skill first - Data efficiency guidance (default small limits, use filters) - Pluralized `load_skill` → `load_skills` for consistency with `load_tools` ### Token usage reduction - Output serialization layer: strips null/undefined/empty values from tool results - Lowered default `find_*` limit from 100 → 10, max from 1000 → 100 ### System object tool generation - System objects (calendar events, messages, etc.) now generate AI tools - Only workflow-related and favorite-related objects are excluded ### Context window display fix - **Bug**: UI compared cumulative tokens (sum of all turns) against single-request context window → showed 100% after a few turns - **Fix**: Track `conversationSize` (last step's `inputTokens`) which represents the actual conversation history size sent to the model - New `conversationSize` column on thread entity with migration ### Workspace AI instructions - Support for custom workspace-level AI instructions --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> |
||
|
Félix Malfait
|
2e104c8e76 |
feat(ai): add code interpreter for AI data analysis (#16559)
## 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 --> |
||
|
Félix Malfait
|
4f91b48470 |
feat(ai): add context usage display to AI chat (BREAKING: deploy server first) (#16518)
## Summary - Add a context usage indicator to the AI chat interface inspired by Vercel's AI SDK Context component - Display token consumption, context window utilization percentage, and estimated cost in credits - Show a circular progress ring with percentage, revealing detailed breakdown on hover ## Changes ### Backend - Stream usage metadata (tokens, model config) via `messageMetadata` callback in `agent-chat-streaming.service.ts` - Return model config from `chat-execution.service.ts` - Add usage and model types to `ExtendedUIMessage` metadata ### Frontend - New `ContextUsageProgressRing` component - circular SVG progress indicator - New `AIChatContextUsageButton` component with hover card showing: - Progress bar with used/total tokens - Input/output token counts with credit costs - Total credits consumed - Track cumulative usage in Recoil state (`agentChatUsageState`) - Reset usage when creating new chat thread - Integrate button into `AIChatTab` ## Test plan - [ ] Open AI chat and send a message - [ ] Verify the context usage button appears with percentage - [ ] Hover over the button to see detailed breakdown - [ ] Verify credits are calculated correctly - [ ] Create a new chat thread and verify usage resets to 0 |
||
|
Félix Malfait
|
e7ebf51e50 |
Replace agent handoff system with planning-based router (#16003)
## 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 |
||
|
Félix Malfait
|
a281f2a773 |
feat: add configurable response format for AI agents (text/JSON) (#15953)
## 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
|
||
|
Félix Malfait
|
f9c61833ec |
Add debug info in AI chat (#15758)
## 🐛 Critical Bug Fix ### Cost Calculation Error (1000x undercharge) - **Fixed**: Cost conversion utility was calculating credits at 1/1000th of actual value - **Before**: `cents * 10` ❌ - **After**: `(cents / 100) * DOLLAR_TO_CREDIT_MULTIPLIER` ✅ - **Impact**: Users were being undercharged by 1000x - Example: 0.75 cents should = 7,500 credits - Bug calculated it as 7.5 credits --- ## 🎯 Code Centralization & DRY ### Unified Cost Calculation - Centralized all cost conversions to use `convertCentsToBillingCredits` utility - Refactored 3 different implementations into 1 single source of truth - Files updated: - `ai-billing.service.ts` - `agent-streaming.service.ts` (2 usages) **Before** (multiple implementations): ```typescript // Wrong implementation const credits = cents * 10; // Verbose implementation const costInDollars = costInCents / 100; const creditsUsed = Math.round(costInDollars * DOLLAR_TO_CREDIT_MULTIPLIER); ``` **After** (unified): ```typescript const creditsUsed = Math.round(convertCentsToBillingCredits(costInCents)); ``` --- ## ✨ UI Component Refactoring ### RoutingDebugDisplay.tsx - **Reduced from 118 lines to 34 lines** (71% reduction) - Extracted `renderTimingRow` helper to eliminate 15 repetitive JSX blocks - Added `formatTokenBreakdown` helper for token display logic - Much easier to add new debug metrics **Before**: 15 nearly-identical blocks of repetitive JSX **After**: Clean, DRY implementation with reusable helpers --- ## 🧹 Code Quality Improvements ### Removed Debug Code - Removed `console.log` accidentally left in `RoutingStatusDisplay.tsx` ### Cleaned Up Comments (18+ removed) Removed redundant comments that stated the obvious: - ❌ "Calculate routing cost if we have token usage" - ❌ "Send the updated routing status with execution metrics to the client" - ❌ "Count tool calls in the response" - ❌ "AI SDK's LanguageModelUsage uses inputTokens/outputTokens" - And 14+ more... Kept meaningful comments: - ✅ "Timing is optional, ignore errors" (explains catch block) - ✅ Type definition grouping comments --- ## 📊 Statistics **Files Modified**: 10 - `convert-cents-to-billing-credits.util.ts` (fixed formula) - `ai-billing.service.ts` (use centralized utility) - `agent-streaming.service.ts` (use utility, remove comments) - `agent-execution.service.ts` (remove comments) - `ai-router.service.ts` (remove comments) - `RoutingStatusDisplay.tsx` (remove debug code) - `RoutingDebugDisplay.tsx` (major refactor) ⭐ - `isDebugModeState.ts` (new file) - `DataMessagePart.ts` (type extensions) - `useClientConfig.ts` (debug mode support) **Impact**: - Lines removed: ~130 (redundant code + comments) - Lines added: ~45 (helper functions) - **Net reduction**: ~85 lines - **Bug fixes**: 1 critical (1000x cost error) - **Centralizations**: 3 locations now using shared utility - **Major refactors**: 1 UI component (71% reduction) --- ## ✅ Verification - ✅ All linter checks pass - ✅ All tests pass (`ai-billing.service.spec.ts` verified) - ✅ No `any` types in affected code - ✅ No TODO/FIXME markers --- ## 🎯 Principles Applied 1. ✅ **Fix Root Causes, Not Symptoms** - Fixed utility function, then used it everywhere 2. ✅ **DRY (Don't Repeat Yourself)** - Centralized cost calculation and UI rendering 3. ✅ **Single Source of Truth** - One place for cost conversion formula 4. ✅ **Code as Documentation** - Removed comments that repeated what code says 5. ✅ **Composability** - Created reusable helper functions 6. ✅ **Type Safety** - Maintained strict typing throughout |
||
|
Abdul Rahman
|
32558673c6 |
feat: Implement AI Router for Dynamic Agent Selection (#15227)
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> |
||
|
Abdul Rahman
|
2685f4a5b9 |
Restructure agent chat messages with parts-based architecture (#14749)
Co-authored-by: Félix Malfait <felix@twenty.com> |
||
|
Abdul Rahman
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216d72b5d7 |
AI SDK v5 migration (#14549)
Co-authored-by: Félix Malfait <felix@twenty.com> |
