Fixes https://github.com/coder/coder/issues/23910
Adds periodic cleanup of chats and chat files to the dbpurge background
goroutine, with a configurable retention period exposed in the Agent
settings UI.
> 🤖 Written by a Coder Agent. Reviewed by a human.
Audit and connection log pages were timing out due to expensive COUNT(*)
queries over large tables. This commit adds opt-in count capping: requests can
return a `count_cap` field signaling that the count was truncated at a threshold,
avoiding full table scans that caused page timeouts.
Text-cast UUID comparisons in regosql-generated authorization queries
also contributed to the slowdown by preventing index usage for connection
and audit log queries. These now emit native UUID operators.
Frontend changes handle the capped state in usePaginatedQuery and
PaginationWidget, optionally displaying a capped count in the pagination
UI (e.g. "Showing 2,076 to 2,100 of 2,000+ logs")
Related to:
https://linear.app/codercom/issue/PLAT-31/connectionaudit-log-performance-issue
Needed by #23833
Adds a `chat_file_links` association table to track which files are
associated with each chat.
- `AppendChatFileIDs` query links a file to a chat with deduplication
- `GetChatFileMetadataByIDs` query returns lightweight file metadata by
IDs
- Tool-created files (e.g. `propose_plan`) are linked to the chat after
insert
- User-uploaded files are linked to the chat when the referencing
message is sent
- Single-chat GET endpoint hydrates `files: ChatFileMetadata[]` on the
response
> 🤖 Created by Coder Agents and massaged into shape by a human.
The backend (`chatd.go`) already fully implements both `"queue"` and
`"interrupt"` busy behaviors for `SendMessage`, and the `message_agent`
subagent tool already leverages both internally. However the HTTP API
hardcoded `"queue"` and the SDK had no way for callers to request
interrupt-on-send.
This adds a `ChatBusyBehavior` enum type to the SDK and an optional
`busy_behavior` field on `CreateChatMessageRequest`. The HTTP handler
validates the field and passes it through to `chatd.SendMessage`.
Default remains `"queue"` for full backward compatibility.
<details><summary>Implementation notes</summary>
- `codersdk/chats.go`: New `ChatBusyBehavior` type with
`ChatBusyBehaviorQueue` and `ChatBusyBehaviorInterrupt` constants. Added
`BusyBehavior` field to `CreateChatMessageRequest` with `enums` tag for
codegen.
- `coderd/exp_chats.go`: `postChatMessages` now reads
`req.BusyBehavior`, maps SDK constants to
`chatd.SendMessageBusyBehavior*`, returns 400 on invalid values.
- `site/src/api/typesGenerated.ts`: Auto-generated via `make gen`.
- No frontend behavior changes — the field is available but unused by
the UI.
</details>
> [!NOTE]
> Generated by Coder Agents
Adds a `system_prompt` field to `CreateChatRequest` that allows API
consumers to provide custom instructions when creating a chat. The
per-chat prompt is stored as a separate system message (`role=system`,
`visibility=model`) in the `chat_messages` table, inserted between the
deployment system prompt and the workspace awareness message.
Also moves deployment system prompt resolution from the HTTP handler
(`resolvedChatSystemPrompt`) into `chatd.CreateChat` where it belongs.
The handler no longer assembles system prompts —
`CreateOptions.SystemPrompt` is now purely the per-chat user prompt, and
the deployment prompt is resolved internally by chatd.
No database schema changes required.
**Message insertion order:**
1. Deployment system prompt (resolved by chatd, existing)
2. Per-chat user system prompt (new, from `CreateOptions.SystemPrompt`)
3. Workspace awareness (existing)
4. Initial user message (existing)
🤖 Generated with [Coder Agents](https://coder.com/agents)
Surface the aggregated `runtime_ms` from `chat_messages` through all
four cost analytics queries (summary, per-model, per-chat, per-user).
This is the key billing metric for agent compute time.
The per-chat breakdown already groups by `root_chat_id`, so subagent
runtime is automatically rolled up under the parent chat — no additional
query changes needed.
<details>
<summary>Implementation details</summary>
**SQL** (`coderd/database/queries/chats.sql`): Added
`COALESCE(SUM(cm.runtime_ms), 0)::bigint AS total_runtime_ms` to
`GetChatCostSummary`, `GetChatCostPerModel`, `GetChatCostPerChat`, and
`GetChatCostPerUser`.
**Go SDK** (`codersdk/chats.go`): Added `TotalRuntimeMs int64` to
`ChatCostSummary`, `ChatCostModelBreakdown`, `ChatCostChatBreakdown`,
and `ChatCostUserRollup`.
**Handler** (`coderd/exp_chats.go`): Wired the new field through all
converter functions and the response assembly.
**Tests** (`coderd/exp_chats_test.go`): Updated fixture to seed non-zero
`runtime_ms` values and added assertions for the new field at summary,
per-model, and per-chat levels.
</details>
> 🤖 Generated by Coder Agents
Polishes the AI model configuration form (add/edit model) with tighter
layout and better input affordances.
**Frontend changes:**
- Replace "Unset" with "Default" in select dropdowns to communicate
system fallback
- Show pricing fields inline instead of behind a collapsible toggle
- Use flat section dividers (`border-t`) instead of bordered fieldsets
- Move field descriptions into info-icon tooltips to fix input
misalignment
- Add InputGroup adornments: `$` prefix + `/1M` suffix on pricing,
`tokens` suffix on token fields, `%` suffix on compression threshold,
range placeholders on temperature/penalty fields
- Shorter pricing labels (Input, Output, Cache Read, Cache Write)
- Compact JSON textareas (1-row height, resizable)
- Smart grid layouts by field type (3-col provider, 4-col pricing, 3-col
advanced)
- Boolean fields render as a segmented control (Default · On · Off)
instead of a dropdown
**Backend changes:**
- Add `enum` tags to OpenAI `service_tier`
(`auto,default,flex,scale,priority`) and `reasoning_summary`
(`auto,concise,detailed`) so they render as select dropdowns instead of
free-text inputs
> 🤖 Generated by Coder Agents
Following on from #23989#24018
- We also no longer want to collect `IsBusiness` demographic data
- Newsletter fields no longer allow `nil` as a value, instead default to
false
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
New `IndustryType` and `OrgSize` enums were added in #23989, but they
are no longer desired in the onboarding/marketing telemetry data. This
removes them.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Two new columns added to aibridge_token_usages:
- cache_read_input_tokens (BIGINT, default 0)
- cache_write_input_tokens (BIGINT, default 0)
Migration backfills existing rows by extracting values from the metadata
JSONB column (cache_read_input, input_cached, prompt_cached for reads
(max value selected since only 1 should be set), cache_creation_input
for writes).
All references to data from metadata were updated to reference new
columns. No other changes then changing where data is extracted from.
Requires aibridge library version bump to include:
https://github.com/coder/aibridge/pull/229
Fixes: https://github.com/coder/aibridge/issues/150
Add optional demographic and newsletter preference fields to the setup
page: business use (yes/no), industry type, organization size, and two
newsletter toggles (marketing, release/security updates).
The new data flows through telemetry via a FirstUserOnboarding struct in
the snapshot payload, sent once when the first user is created. The
telemetry-server and BigQuery schema changes are required separately to
persist this data.
---------
Co-authored-by: default <davidiii@fraley.us>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Frontend for provider key policies (backend in #23751).
## Changes
**Admin provider form**: Three policy toggles (central API key, user API
keys, central fallback) with cross-field validation and conditional
visibility. Form resets properly after save.
**User settings page**: New `/settings/providers` route for personal API
key management. Conditional sidebar item (visible only when providers
allow user keys). Status badges, masked key input, save/remove actions
with confirmation. Read-only model list per provider. Gated behind
`agents` experiment flag.
**Model selector**: Distinguishes user-fixable (`user_api_key_required`)
from admin-fixable (`missing_api_key`) empty states. Links to
`/settings/providers` when user action is needed. Applied to both chat
detail and agent create flows.
**API client**: Query/mutation hooks for user provider configs. Cache
invalidation across provider configs and model catalog.
When a user tries to archive-and-delete a chat from /agents but the
workspace is already gone, the UI showed a "Failed to look up workspace
for deletion" toast and blocked the archive. This change detects the
workspace-gone response and archives the chat without attempting
deletion.
## Changes
The backend returns 410 Gone for soft-deleted workspaces and 404 for
workspaces that do not exist or the user cannot access.
`isWorkspaceNotFound()` detects both status codes.
`resolveArchiveAndDeleteAction()` now returns `"archive"` when the
workspace preflight fetch gets a 404 or 410, and the page branches on
that action to call the existing archive mutation directly. The
`archiveAndDeleteMutation` also tolerates these status codes from
`deleteWorkspace()` to handle the race where the workspace disappears
between the preflight lookup and the actual delete call.
The mutation body was extracted into a testable
`archiveAndDeleteWorkspace()` utility so the tolerance logic has direct
test coverage. A `navigateAfterArchive()` helper consolidates the
post-archive redirect logic that was previously duplicated across the
proceed, confirm, and archive paths.
## Pre-existing patterns preserved
- The `"proceed"` and `"confirm"` archive-and-delete paths use
`onSettled` for navigation, matching the existing behavior before this
change. Only the new `"archive"` path uses `onSuccess` since it has no
workspace deletion step that should still navigate on partial failure.
- `isWorkspaceNotFound()` uses the same `isAxiosError(error) &&
error.response?.status` pattern already used in several places in
`site/src/api/api.ts`. The backend 404 ambiguity (deleted vs
unauthorized) is documented in the JSDoc.
- The pre-existing double-submit race during the async preflight window
is unchanged.
## Description
Adds `provider_name` to aibridge interceptions to store the provider
instance name alongside the provider type. This allows distinguishing
between multiple instances of the same provider type (e.g. `copilot` vs
`copilot-business`).
## Changes
* Add `provider_name` column to `aibridge_interceptions` table with
backfill from `provider`.
* Add `provider_name` field to the proto `RecordInterceptionRequest`
message.
* Add `ProviderName` to the `codersdk.AIBridgeInterception` API
response.
_Disclaimer: initially produced by Claude Opus 4.6, modified and
reviewed by @ssncferreira ._
## Add terminal panel to chat sidebar
Extract the reusable terminal runtime from `TerminalPage` into
`modules/terminal/` and wire it into the agents chat right sidebar as a
new **Terminal** tab.
### Changes
- **`modules/terminal/WorkspaceTerminal.tsx`** — Shared xterm +
websocket terminal component (container-sized, no route dependency)
- **`modules/terminal/WorkspaceTerminalAlerts.tsx`** — Moved from
`TerminalPage/` to shared module
- **`pages/AgentsPage/TerminalPanel.tsx`** — Sidebar wrapper around
`WorkspaceTerminal`
- **`pages/AgentsPage/AgentDetailView.tsx`** — Terminal tab added (gated
on `hasWorkspace`)
- **`pages/TerminalPage/TerminalPage.tsx`** — Slimmed to page-shell
using shared component
### Demo
[dogfood-terminal-demo.webm](https://github.com/user-attachments/assets/359200dc-f8e4-4a9a-b00b-923f142dc228)
### Behavior
- Terminal tab appears only when the chat has a workspace with a
connected agent
- Connects via the existing workspace agent PTY websocket
- Resizes correctly on panel width changes, expand/collapse, and
viewport resize
- `fitAddon.fit()` guarded against pre-renderer crashes (fixes proxy
access)
- Tab switching unmounts/remounts cleanly (reconnects via session token)
- No changes to Git or Desktop panel behavior
---
_Generated with [`mux`](https://github.com/coder/mux) • Model:
`anthropic:claude-opus-4-6` • Thinking: `xhigh`_
- Add `chat-access` built-in role granting chat CRUD at User scope
- Exclude `ResourceChat` from member, org member, and org service
account `allPermsExcept` calls
- Allow system, owner, and user-admin to assign the new role
- Migration auto-assigns role to users who have ever created a chat
- Update RBAC test matrix: `memberMe` denied, `chatAccessUser` allowed
**Breaking change**: Members without `chat-access` lose chat creation
ability. Migration covers existing chat creators. Members who have never
created a chat do not get this role automatically applied.
> 🤖 This PR was created by a Coder Agent and reviewed by me.
Adds a nullable JSONB column `last_injected_context` to the `chats`
table that stores the most recently persisted injected context parts
(AGENTS.md context-file and skill message parts). The column is updated
only when `persistInstructionFiles()` runs — on first workspace attach
or when the agent changes — so there are no redundant writes on
subsequent turns.
Internal fields (`ContextFileContent`, `ContextFileOS`,
`ContextFileDirectory`, `SkillDir`) are stripped at write time so the
column only holds small metadata. No stripping needed on the read path.
<details>
<summary>Implementation notes</summary>
- New migration `000456` adds nullable `last_injected_context JSONB`
column.
- New SQL query `UpdateChatLastInjectedContext` writes the column
without touching `updated_at`.
- `persistInstructionFiles()` strips internal fields from parts via
`StripInternal()` before persisting.
- Sentinel path (no AGENTS.md) persists skill-only parts when skills
exist.
- `codersdk.Chat` exposes `LastInjectedContext []ChatMessagePart`
(omitempty).
- `db2sdk.Chat()` passes through the already-clean data.
</details>
## Summary
Skills are now discovered once on the first turn (or when the workspace
agent changes) and persisted as `skill` message parts alongside
`context-file` parts. On subsequent turns, the skill index is
reconstructed from persisted parts instead of re-dialing the workspace
agent.
This makes skills consistent with the AGENTS.md pattern and is
groundwork for a future `/context` endpoint that surfaces loaded
workspace context to the frontend.
## Changes
- Add `skill` `ChatMessagePartType` with `SkillName` and
`SkillDescription` fields
- Extend `persistInstructionFiles` to also discover and persist skills
as parts
- Add `skillsFromParts()` to reconstruct skill index from persisted
parts on subsequent turns
- Update `runChat()` to use `skillsFromParts` instead of re-dialing
workspace for skills
- Frontend: handle new `skill` part type (skip rendering, hide
metadata-only messages)
## Before / After
| | AGENTS.md | Skills |
|---|---|---|
| **Before** | Persist as `context-file` parts, reconstruct from parts |
In-memory `skillsCache` only, re-dial workspace on cache miss |
| **After** | Persist as `context-file` parts, reconstruct from parts |
Persist as `skill` parts, reconstruct from parts |
The in-memory `skillsCache` remains for `read_skill`/`read_skill_file`
tool calls that need full skill bodies on demand.
<details><summary>Design context</summary>
This is the first step toward a unified workspace context
representation. Currently:
- Context files are persisted as message parts (works)
- Skills were only in-memory (inconsistent)
- Workspace MCP servers are cached in-memory (future work)
Persisting skills as parts means a future `/context` endpoint can query
both context files and skills from the same message parts in the DB,
without depending on ephemeral server-side caches.
</details>
Closes#22136
This pull-request implements a `<ClientFilter />` to our `Request Logs`
page for AI Bridge. This will allow the user to select a client which
they wish to filter against. Technically the backend is able to actually
filter against multiple clients at once however the frontend doesn't
currently have a nice way of supporting this (future improvement).
<img width="1447" height="831" alt="image"
src="https://github.com/user-attachments/assets/0be234e2-25f2-4a89-b971-d74817395da1"
/>
---------
Co-authored-by: Jeremy Ruppel <jeremy.ruppel@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
Adds read/unread tracking for chats so users can see which agent
conversations have new assistant messages they haven't viewed.
## Backend Changes
- Adds `last_read_message_id` column to the `chats` table (migration
000439).
- Computes `has_unread` as a virtual column in `GetChatsByOwnerID` using
an `EXISTS` subquery checking for assistant messages beyond the read
cursor.
- Exposes `has_unread` on the `codersdk.Chat` struct and auto-generated
TypeScript types.
- Updates `last_read_message_id` on stream connect/disconnect in
`streamChat`, avoiding per-message API calls during active streaming.
- Uses `context.WithoutCancel` for the deferred disconnect write so the
DB update succeeds even after the client disconnects.
## Frontend Changes
- Bold title (`font-semibold`) for unread chats in the sidebar.
- Small blue dot indicator next to the relative timestamp.
- Suppresses unread indicator for the currently active chat via
`isActive` from NavLink.
## Design Decisions
- Only `assistant` messages count as unread — the user's own messages
don't trigger the indicator.
- No foreign key on `last_read_message_id` since messages can be deleted
(via rollback/truncation) and the column is just a high-water mark.
- Zero API calls during streaming: exactly 2 DB writes per stream
session (connect + disconnect).
- Unread state refreshes on chat list load and window focus. The
`watchChats` WebSocket optimistically marks non-active chats as unread
on `status_change` events, but does not carry a server-computed
`has_unread` field. Navigating to a chat optimistically clears its
unread indicator in the cache.
Add a per-MCP-server `model_intent` toggle that wraps tool schemas with
a
`model_intent` field, requiring the LLM to provide a human-readable
description of each tool call's purpose. The intent string is shown as a
status label in the UI instead of opaque tool names, and is
transparently
stripped before the call reaches the remote MCP server.
Built-in tools have rich specialized renderers (terminal blocks, file
diffs,
etc.) and don't need this. MCP tools hit `GenericToolRenderer` which
only
shows raw tool names and JSON — that's where model_intent adds value.
The model learns what to provide via the JSON Schema `description` on
the
`model_intent` property itself — no system prompt changes needed.
<details>
<summary>Implementation details</summary>
### Architecture
Inspired by the `withModelIntent()` pattern from `coder/blink`, adapted
for
Go + React. The wrapping is entirely in the `mcpclient` layer — tool
implementations never see `model_intent`.
**Schema wrapping** (`mcpToolWrapper.Info()`): When enabled, wraps the
original tool parameters under a `properties` key and adds a
`model_intent`
string field with a rich description that teaches the model inline.
**Input unwrapping** (`mcpToolWrapper.Run()`): Strips `model_intent` and
unwraps `properties` before forwarding to the remote MCP server. Handles
three input shapes models may produce:
1. `{ model_intent, properties: {...} }` — correct format
2. `{ model_intent, key: val, ... }` — flat, no wrapper
3. Malformed — falls through gracefully
**Frontend extraction**: `streamState.ts` extracts `model_intent` from
incrementally parsed streaming JSON. `messageParsing.ts` extracts it
from
persisted tool call args.
**UI rendering**: `GenericToolRenderer` shows the capitalized intent
string
as the primary label when available, falling back to the raw tool name.
### Changes
- Database: `model_intent` boolean column on `mcp_server_configs`
- SDK: `ModelIntent` field on config/create/update types
- API: pass-through in create/update handlers + converter
- mcpclient: schema wrapping in `Info()`, input unwrapping in `Run()`
- Frontend: extraction from streaming + persisted args
- UI: intent label in `GenericToolRenderer`, toggle in admin panel
- Tests: 6 new tests (schema wrapping, unwrapping, passthrough,
fallback)
### Decision log
- **Option lives on MCPServerConfig, not model config**: Built-in tools
already have rich renderers; only MCP tools benefit from model_intent.
- **No system prompt changes**: The JSON Schema `description` on the
`model_intent` property teaches the model inline.
- **Pointer bool on update request**: Follows existing pattern (`*bool`)
so PATCH requests don't reset the value when omitted.
</details>
## Summary
Fixes two issues with pinned chat drag-to-reorder in the agents sidebar:
1. **Missing optimistic reorder**: After dragging a pinned chat to a new
position, the sidebar flashed back to the old order while waiting for
the server response, then snapped to the new order. Now the react-query
cache is updated optimistically in `onMutate` so the reorder is visually
instant.
2. **Post-drag navigation on upward drag**: Dragging a pinned chat
**upward** caused unintended navigation to that chat on drop. The
browser synthesizes a click from the final `pointerup`, and the previous
suppression listener was attached too low in the DOM tree to reliably
intercept it after items reorder.
## Changes
### Optimistic cache reorder (`site/src/api/queries/chats.ts`)
`reorderPinnedChat.onMutate` now extracts all pinned chats from the
cache, reorders them to match the new position, and writes the updated
`pin_order` values back via `updateInfiniteChatsCache`. The server
response still reconciles on `onSettled`.
### Document-level click suppression (`AgentsSidebar.tsx`)
- Moved click suppression from the pinned container div to
`document.addEventListener('click', handler, true)`. This fires before
any element-level handlers regardless of DOM reordering during drag.
Scoped via `pinnedContainerRef.contains(target)` so it only affects
pinned rows.
- Replaced `recentDragRef` (boolean cleared by `setTimeout(100ms)`) with
`lastDragEndedAtRef` (`performance.now()` timestamp checked against a
300ms window). This eliminates the race where the timeout fires before
the synthetic click arrives.
---
PR generated with Coder Agents
## Summary
Add site-wide banners for AI Governance seat usage thresholds:
1. **90% capacity warning (admin-only):** When actual AI Governance
seats are ≥90% and <100% of the license limit, admins see:
> "You have used 90% of your AI governance add-on seats."
2. **Over-limit banner (admin-only):** When actual seats exceed the
license limit, admins see a prominent warning:
> "Your organization is using {actual} / {limit} AI Governance user
seats ({X}% over the limit). Contact sales@coder.com"
- Uses floor whole percentage (Go int division / `Math.floor`)
- Includes a clickable `mailto:sales@coder.com` link
## Summary
Adds a "Generate new title" action that lets users manually regenerate a
chat's title using richer conversation context than the automatic
first-message title path.
## Changes
### Backend
- **New endpoint:** `POST
/api/experimental/chats/{chatID}/title/regenerate` returns the updated
Chat with a regenerated title
- **Manual title algorithm:** Extracts useful user/assistant text turns
→ selects first user turn + last 3 turns → builds context with gap
markers → renders prompt with anti-recency guidance → calls lightweight
model → normalizes output
- **Helpers:** `extractManualTitleTurns`,
`selectManualTitleTurnIndexes`, `buildManualTitleContext`,
`renderManualTitlePrompt`, `generateManualTitle` — all private, with the
public `Server.RegenerateChatTitle` method
- **SDK:** `ExperimentalClient.RegenerateChatTitle(ctx, chatID) (Chat,
error)`
- Persists title via existing `UpdateChatByID` and broadcasts
`ChatEventKindTitleChange`
### Frontend
- API client method + React Query mutation with cache invalidation
- "Generate new title" menu item (with wand icon) in both TopBar and
Sidebar dropdown menus
- Loading/disabled state while regeneration is in-flight
- Error toast on failure
- Stories updated for both menus
### Tests
- `quickgen_test.go`: Table-driven tests for all 4 helper functions
(turn extraction, index selection, context building, prompt rendering)
- `exp_chats_test.go`: Handler tests (ChatNotFound,
NotFoundForDifferentUser, NoDaemon)
## Design notes
- The existing auto-title path (`maybeGenerateChatTitle`, `titleInput`)
is completely unchanged
- Manual regeneration uses richer context (first user turn + last 3
turns + gap markers) vs the auto path's single first message
- Endpoint is experimental and marked with `@x-apidocgen {"skip": true}`
https://github.com/user-attachments/assets/bd5d12a1-61b3-4b7d-83b6-317bdfb60b3c
## Summary
Adds pinned chats to the agents page sidebar with server-side
persistence and drag-to-reorder. Users can pin/unpin chats via the
context menu, and pinned chats appear in a dedicated "Pinned" section
above the time-grouped list.
## Database
Migration `000453_chat_pin_order`: adds `pin_order integer DEFAULT 0 NOT
NULL` column on `chats` (0 = unpinned, 1+ = pinned in display order).
Three SQL queries handle pin operations server-side using CTEs with
`ROW_NUMBER()`:
- `PinChatByID`: normalizes existing orders and appends to end
- `UnpinChatByID`: sets target to 0 and compacts remaining pins
- `UpdateChatPinOrder`: shifts neighbors, clamps to `[1, pinned_count]`
All queries exclude archived chats. `ArchiveChatByID` clears `pin_order`
on archive. The handler rejects pinning archived chats with 400.
## Backend
Pin/unpin/reorder go through the existing `PATCH
/api/experimental/chats/{chat}` via the `pin_order` field on
`UpdateChatRequest`. The handler routes based on current pin state:
`pin_order == 0` unpins, `> 0` on an already-pinned chat reorders, `> 0`
on an unpinned chat appends to end.
## Frontend
- `pinChat` / `unpinChat` / `reorderPinnedChat` optimistic mutations
using shared `isChatListQuery` predicate
- Sidebar renders Pinned section above time groups, excludes pinned
chats from time groups
- Pin/Unpin context menu items (hidden for child/delegated chats)
- `@dnd-kit/core` + `@dnd-kit/sortable` for drag-to-reorder with
`MouseSensor`, `TouchSensor`, and `KeyboardSensor`
- Local pin-order override prevents flash on drop; click blocker
prevents NavLink navigation after drag
---
*PR generated with Coder Agents*
Admins can now control whether the built-in Coder Agents default system
prompt is prepended to their custom instructions, rather than having the
custom prompt silently replace the default.
**Changes:**
- New `include_default_system_prompt` boolean toggle (defaults to `true`
for existing deployments) stored as a site config key — no migration
needed.
- GET `/api/experimental/chats/config/system-prompt` returns the toggle
state, the custom prompt, and a preview of the built-in default.
- PUT persists both the toggle and custom prompt atomically in a single
transaction.
- `resolvedChatSystemPrompt()` composes `[default?, custom?]` joined by
`\n\n`, falling back to the built-in default on DB errors.
- Settings UI adds a Switch toggle with conditional helper text and a
"Preview" button that shows the built-in default prompt via the existing
`TextPreviewDialog`.
- Comprehensive test coverage: 15 subtests covering toggle behavior,
prompt composition matrix, auth boundaries, and integration with chat
creation.
- Adds `GET /api/experimental/chats/by-workspace` endpoint that returns
workspace_id → latest chat_id mapping
- Modifies FE to fetch this alongside the workspace list, gated on
`agents` experiment and render an "Agent" badge similar to the existing
"Task" badge in `WorkspacesTable`
- Badge links to the "latest chat" linked to the given workspace.
Notes:
- Intentionally uses `fetchWithPostFilter` for RBAC to decouple from
workspaces API — will migrate to `workspaces_expanded` view later.
- If users have multiple chats linked to the same workspace, the badge
will link to the most recently updated one.
> 🤖 This PR was created with the help of Coder Agents, and has been
reviewed by my human. 🧑💻
## Summary
Adds an entitlement-gated **AI add-on** column to both the **Users**
table and the **Organization Members** table. When
`ai_governance_user_limit` is entitled, each row shows whether the user
is consuming an AI seat.
## Background
The AI governance add-on tracks which users are consuming AI seats.
Admins need visibility into per-user seat consumption directly from the
user management tables. This change surfaces that information through
both the site-wide Users table and the per-organization Members table,
gated behind the `ai_governance_user_limit` entitlement so the column
only appears when the feature is licensed.
## Implementation
### Backend
- **New SQL query** `GetUserAISeatStates`
(`coderd/database/queries/aiseatstate.sql`) — returns user IDs consuming
an AI seat, derived from:
- Users with entries in `aibridge_interceptions` (AI Bridge usage)
- Users who own workspaces with `has_ai_task = true` builds (AI Tasks
usage)
- **SDK types** — added `has_ai_seat: boolean` to `codersdk.User` and
`codersdk.OrganizationMemberWithUserData`
- **Handler wiring** — both the Users list endpoint (`coderd/users.go`)
and all Members endpoints (`coderd/members.go`) query AI seat state per
page of user IDs and populate the response field
- **dbauthz** — per-user `ActionRead` checks on `ResourceUserObject`
### Frontend
- **Shared `AISeatCell` component**
(`site/src/modules/users/AISeatCell.tsx`) — green `CircleCheck` for
consuming, gray `X` for non-consuming
- **`TableColumnHelpTooltip`** — extended with `ai_addon` variant with
tooltip: *"Users with access to AI features like AI Bridge, Boundary, or
Tasks who are actively consuming a seat."*
- **Column visibility** gated behind
`useFeatureVisibility().ai_governance_user_limit`
## Validation
- Backend: dbauthz full method suite (`TestMethodTestSuite`) passes
including new `GetUserAISeatStates` test
- Backend: `TestGetUsers`, `TestUsersFilter`, CLI golden file tests pass
- Frontend: 7/7 tests pass across `UsersPage.test.tsx` and
`OrganizationMembersPage.test.tsx` (column visibility gating both
directions)
- `go build ./coderd/...` compiles clean
- `pnpm --dir site run lint:types` passes
- `make gen` clean
## Risks
- **Pagination performance**: The AI seat query is scoped to the current
page's user IDs (not a full table scan), keeping it efficient for
paginated views.
- **Semantic scope**: The workspace-side AI seat derivation uses "any
build with `has_ai_task = true`" rather than "latest build only". If the
product intent is latest-build-only, this can be tightened in a
follow-up.
---
_Generated with `mux` • Model: `anthropic:claude-opus-4-6` • Thinking:
`xhigh` • Cost: `$27.25`_
<!-- mux-attribution: model=anthropic:claude-opus-4-6 thinking=xhigh
costs=27.25 -->
## Summary
This change removes the steady-state "resolve the latest workspace
agent" query from chat execution.
Instead of asking the database for the latest build's agent on every
turn, a chat now persists the workspace/build/agent binding it actually
uses and reuses that binding across subsequent turns. The common path
becomes "load the bound agent by ID and dial it", with fallback paths to
repair the binding when it is missing, stale, or intentionally changed.
## What changes
- add `workspace_id`, `build_id`, and `agent_id` binding fields to
`chats`
- expose those fields through the chat API / SDK so the execution
context is explicit
- load the persisted binding first in chatd, instead of always resolving
the latest build's agent
- persist a refreshed binding when chatd has to re-resolve the workspace
agent
- keep child / subagent chats on the same bound workspace context by
inheriting the parent binding
- leave `build_id` / `agent_id` unset for flows like `create_workspace`,
then bind them lazily on the next agent-backed turn
## Runtime behavior
The binding is treated as an optimistic cache of the agent a chat should
use:
- if the bound agent still exists and dials successfully, we use it
without a latest-build lookup
- if the bound agent is missing or no longer reachable, chatd
re-resolves against the latest build and persists the new binding
- if a workspace mutation changes the chat's target workspace, the
binding is updated as part of that mutation
To avoid reintroducing a hot-path query, dialing uses lazy validation:
- start dialing the cached agent immediately
- only validate against the latest build if the dial is still pending
after a short delay
- if validation finds a different agent, cancel the stale dial, switch
to the current agent, and persist the repaired binding
## Result
The hot path stops issuing
`GetWorkspaceAgentsInLatestBuildByWorkspaceID` for every user message,
which is the source of the DB pressure this PR is addressing. At the
same time, chats still converge to the correct workspace agent when the
binding becomes stale due to rebuilds or explicit workspace changes.
## Summary
Adds a general-purpose `map[string]string` label system to chats, stored
as jsonb with a GIN index for efficient containment queries.
This is a standalone foundational feature that will be used by the
upcoming Automations feature for session identity (matching webhook
events to existing chats), replacing the need for bespoke session-key
tables.
## Changes
### Database
- **Migration 000451**: Adds `labels jsonb NOT NULL DEFAULT '{}'` column
to `chats` table with a GIN index (`idx_chats_labels`)
- **`InsertChat`**: Accepts labels on creation via `COALESCE(@labels,
'{}')`
- **`UpdateChatByID`**: Supports partial update —
`COALESCE(sqlc.narg('labels'), labels)` preserves existing labels when
NULL is passed
- **`GetChats`**: New `has_labels` filter using PostgreSQL `@>`
containment operator
- **`GetAuthorizedChats`**: Synced with generated `GetChats` (new column
scan + query param)
### API
- **Create chat** (`POST /chats`): Accepts optional `labels` field,
validated before creation
- **Update chat** (`PATCH /chats/{chat}`): Supports `labels` field for
atomic label replacement
- **List chats** (`GET /chats`): Supports `?label=key:value` query
parameters (multiple are AND-ed)
### SDK
- `Chat`, `CreateChatRequest`, `UpdateChatRequest`, `ListChatsOptions`
all gain `Labels` fields
- `UpdateChatRequest.Labels` is a pointer (`*map[string]string`) so
`nil` means "don't change" vs empty map means "clear all"
### Validation (`coderd/httpapi/labels.go`)
- Max 50 labels per chat
- Key: 1–64 chars, must match `[a-zA-Z0-9][a-zA-Z0-9._/-]*` (supports
namespaced keys like `github.repo`, `automation/pr-number`)
- Value: 1–256 chars
- 13 test cases covering all edge cases
### Chat runtime
- `chatd.CreateOptions` gains `Labels` field, threaded through to
`InsertChat`
- Existing `UpdateChatByID` callers (e.g., quickgen title updates) are
unaffected — NULL labels preserve existing values via COALESCE
We had a bug where computer use base64-encoded screenshots would not be
interpreted as screenshots anymore once saved to the db, loaded back
into memory, and sent to Anthropic. Instead, they would be interpreted
as regular text. Once a computer use agent made enough screenshots and
stopped, and you tried sending it another message, you'd get an out of
context error:
<img width="808" height="367" alt="Screenshot 2026-03-23 at 12 02 54"
src="https://github.com/user-attachments/assets/f0bf6be2-4863-47ca-a7a9-9e6d9dfceeed"
/>
This PR fixes that.
## Summary
Introduces a new `context-file` ChatMessagePart type for persisting
workspace instruction files (AGENTS.md) as durable, frontend-visible
message parts. This is the foundation for showing loaded context files
in the chat input's context indicator tooltip.
### Problem
Previously, instruction files were resolved transiently on every turn
via `resolveInstructions()` → `InsertSystem()` and injected into the
in-memory prompt without persistence. The frontend had no knowledge that
instruction files were loaded into context, and there was no way to
surface this information to users.
### Solution
Instruction files are now read **once** when a workspace is first
attached to a chat (matching how [openai/codex handles
it](https://developers.openai.com/codex/guides/agents-md)) and persisted
as `user`-role, `both`-visibility message parts with a new
`context-file` type. This ensures:
- **Durability**: survives page refresh (data is in the DB, returned by
`getChatMessages`)
- **Cache-friendly**: `user`-role avoids the system-message hoisting
that providers do, keeping the instruction content in a stable position
for prompt caching
- **Frontend-visible**: the frontend receives paths and truncation
status for future context indicator rendering
- **Extensible**: the same pattern works for Skills (future)
### Key changes
| Layer | Change |
|---|---|
| **SDK** (`codersdk/chats.go`) | Add `ChatMessagePartTypeContextFile`
with `context_file_path`, `context_file_content` (internal, stripped
from API), `context_file_truncated` fields |
| **Prompt expansion** (`chatprompt`) | Expand `context-file` parts to
`<workspace-context>` text blocks in `partsToMessageParts()` |
| **Chat engine** (`chatd.go`) | Add `persistInstructionFiles()`, called
on first turn with a workspace. Remove per-turn `resolveInstructions()`
+ `InsertSystem()` from `processChat()` and `ReloadMessages` |
| **Frontend** | Ignore `context-file` parts in `messageParsing.ts` and
`streamState.ts` (no rendering yet — follow-up will add tooltip display)
|
### How it works
1. On each turn, `processChat` checks if any loaded message contains
`context-file` parts
2. If not (first turn with a workspace), reads AGENTS.md files via the
workspace agent connection and persists them
3. For this first turn, also injects the instruction text into the
prompt (since messages were loaded before persistence)
4. On all subsequent turns, `ConvertMessagesWithFiles()` encounters the
persisted `context-file` parts and expands them into text automatically
— no extra resolution needed
- Stores a deployment-wide agents template allowlist in `site_configs`
(`agents_template_allowlist`)
- Adds `GET/PUT /api/experimental/chats/config/template-allowlist`
endpoints
- Filters `list_templates`, `read_template`, and `create_workspace` chat
tools by allowlist, if defined (empty=all allowed)
- Add "Templates" admin settings tab in Agents UI ([what it looks
like](https://624de63c6aacee003aa84340-sitjilsyrr.chromatic.com/?path=/story/pages-agentspage-agentsettingspageview--template-allowlist))
> 🤖 This PR was created with the help of Coder Agents, and has been
reviewed by my human. 🧑💻
> **PR Stack**
>
> 1. #23351 ← `#23282`
> 2. **#23282** ← `#23275` *(you are here)*
> 3. #23275 ← `#23349`
> 4. #23349 ← `main`
---
## Summary
Replaces raw error strings and infinite "Thinking..." spinners in the
agents chat UI with a structured live-status model that drives startup,
retry, and failure UI from one source of truth.
This branch also folds in the frontend follow-up fixes that fell out of
that refactor: malformed `retrying_at` timestamps no longer render
`Retrying in NaNs`, stale persisted generic errors no longer outlive a
recovered chat status, and partial streamed output stays visible when a
response fails after blocks have already rendered.
Consumes the structured error metadata added in #23275.
Retry-After header handling remains in #23351.
<img width="853" height="493" alt="image"
src="https://github.com/user-attachments/assets/5a4a1690-5e22-4ece-965c-a000fd669244"
/>
<img width="812" height="517" alt="image"
src="https://github.com/user-attachments/assets/e78d28ce-1566-48ca-a991-62c6e1838079"
/>
<img width="847" height="523" alt="image"
src="https://github.com/user-attachments/assets/e5fd7b60-4a3c-4573-ba4c-4e5f6dbfbdc3"
/>
## Problem
The previous AgentDetail chat UI derived startup, retry, and failure
behavior from several loosely connected bits of state spread across
`ChatContext`, `AgentDetailContent`, `ConversationTimeline`, and ad hoc
props. That made the UI inconsistent: some failures were just raw
strings, retry state could only partially describe what was happening,
startup could sit on an infinite spinner, and rendering decisions
depended on local booleans instead of one authoritative model.
Those splits also made edge cases brittle. Invalid retry timestamps
could produce broken countdown text, persisted generic errors could
linger after recovery, and streamed partial output could disappear if
the turn later failed.
## Fix
Introduce a structured live-status pipeline for AgentDetail.
`ChatContext` now normalizes stream errors and retry metadata into
richer state, `liveStatusModel` centralizes precedence and phase
derivation, and `ChatStatusCallout` renders startup, retry, and terminal
failure states with shared copy, provider attribution, status links,
attempt metadata, and guarded countdown handling.
`AgentDetailContent` and `ConversationTimeline` now consume that single
model instead of juggling separate error and stream booleans, while
usage-limit messaging stays on its explicit path. The result is a
timeline that shows consistent state transitions, preserves accumulated
assistant output across failures, suppresses stale generic errors once
live state recovers, and has focused model, store, and story coverage
around those behaviors.
When a chat is created, createChat.onSuccess invalidates the sidebar
list query, triggering a background refetch. The refetch can hit the
server before async title generation finishes, returning the fallback
(truncated) title. If the title_change WebSocket event arrives and
writes the generated title into the cache, the in-flight refetch
response then overwrites it with the stale fallback title.
Cancel any in-flight sidebar-list and per-chat refetches before every
WebSocket-driven cache write. This mirrors the existing pattern in
archiveChat/unarchiveChat, which cancel queries before optimistic
updates for the same reason.
Cian Johnston