Previously, the sessions list sorted by `MIN(started_at)` across
interceptions, so sessions with old start times but recent activity
would sink to the bottom of the list regardless of how recently they
were used.
`ListAIBridgeSessions` now sorts by `COALESCE(MAX(prompt.created_at),
MIN(started_at)) DESC`, exposed as the non-nullable `last_active_at`
field. Sessions with prompts surface by last activity; sessions with no
prompts fall back to their start time.
The original implementation used two separate columns (`last_active_at`
as a nullable prompt timestamp and `sort_at` as the non-nullable cursor
key). This revision collapses them into a single `last_active_at` that
is always set — simplifying the SQL, the Go conversion, the API type,
and the frontend.
🤖 Generated with [Claude Code](https://claude.ai/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Drop the `chat_model_configs.provider -> chat_providers.provider`
foreign key and soft-delete model configs when their provider is
removed. The provider row is now hard-deleted inside a transaction that
also tombstones its model configs and promotes a replacement default
when needed.
Historical chats and messages keep pointing at the soft-deleted model
config rows, which are hidden from live/admin queries but still resolve
for read. The runtime chat path already falls back to the default model
config when a soft-deleted config is looked up.
Replaces the lost FK validation in the create/update model-config
handlers with an explicit provider lookup that returns the existing
`Chat provider is not configured.` 400.
## UX
**Admin deleting a chat provider that has historical usage**
- Before: blocked with 400 `Provider models are still referenced by
existing chats.` Admins had no in-product way to remove a provider that
had ever been used.
- After: delete succeeds (204). Any model configs under that provider
are soft-deleted. If the removed provider owned the default model
config, one of the remaining live configs is auto-promoted to the new
default. The promotion is deterministic (`ensureDefaultChatModelConfig`
picks the first live config by `provider ASC, model ASC, updated_at
DESC, id DESC`); there is no picker, and no toast or response detail
names which config became the new default.
**End users with chats that used a deleted provider's model**
- Old chats still open and their history still renders unchanged.
- Sending a new turn in such a chat silently falls back to the current
default model. No banner or warning tells the user the original model is
gone.
- The model picker no longer lists the deleted model.
- If no default model config exists at all after the delete, sending a
new turn fails with `no default chat model config is available`.
**Admin creating or updating a model config against a provider that is
not configured**
- Same as before: 400 `Chat provider is not configured.` Only the
detection mechanism changed (explicit `FOR UPDATE` lookup inside the
transaction, which also serializes against a concurrent provider
delete).
**Admin updating a model config whose row disappears mid-transaction**
- Now returns the standard 404 `Resource not found or you do not have
access to this resource` instead of the previous 500 that leaked `sql:
no rows in result set` in the detail. Unrelated internal races (for
example a race on the promoted default candidate) are still reported as
500 so they are not misclassified as "your target is gone".
Closes CODAGT-23
## Summary
Allow root plan-mode chats to use MCP tools from external servers that
an admin has explicitly approved for plan mode. Workspace MCP and
plan-mode subagents remain blocked.
## Problem
`chatd.go` excluded every MCP tool when `isPlanModeTurn` was true, so
planning had no access to tools like docs search, ticketing, etc.
Lifting that guard wholesale was unsafe: `mcp_server_configs` already
has centralized admin governance, but workspace-local MCP (discovered
from agent `.mcp.json`) does not, and subagents use a narrower trust
boundary.
## Fix
Add an admin-controlled per-server `allow_in_plan_mode` flag (default
`false`) and gate plan-mode MCP access on it.
### Backend / schema
- New migration `000472_mcp_server_allow_in_plan_mode.{up,down}.sql` and
matching fixture update.
- `mcpserverconfigs.sql` + generated code: persist and read the new
column.
- `codersdk/mcp.go`: thread the field through `MCPServerConfig`,
`Create*`, and `Update*` request types.
- `coderd/mcp.go`: validate, persist, and return the flag in
get/list/create/update handlers.
### chatd
- `coderd/x/chatd/chatd.go`: pre-filter selected external MCP configs by
`AllowInPlanMode` before calling `mcpclient.ConnectAll` on plan-mode
root turns. Workspace MCP discovery is skipped entirely on plan-mode
turns.
- Single helper decides whether a tool is available in plan mode, used
both at construction and for active-tool filtering (defense in depth).
Plan-mode subagents, dynamic tools, provider-native tools, computer-use,
and workspace MCP stay unchanged.
- `coderd/x/chatd/prompt.go`: update the root plan-mode overlay text to
match the new boundary.
### UI
- `MCPServerAdminPanel.tsx`: add an explicit toggle ("Allow all tools
from this MCP server in root plan mode") next to the existing governance
controls.
- Regenerated `site/src/api/typesGenerated.ts`.
### Docs
- `docs/ai-coder/agents/architecture.md`: replace the blanket "MCP is
unavailable in plan mode" note with the new root-only, external-only,
admin-approved policy. Explicitly call out that workspace MCP and
plan-mode subagents are still excluded.
### Tests
- Plan-mode visibility (approved vs non-approved external server).
- Plan-mode invocation of an approved external MCP tool.
- End-to-end plan-mode workflow that uses an approved MCP tool and then
reaches `propose_plan`.
- Regressions: workspace MCP still excluded in plan mode; plan-mode
subagents still on the restricted tool boundary; existing tool
allow/deny list filtering still applies.
## Policy precedence
`allow_in_plan_mode` is an **additional** requirement on top of existing
`enabled`, availability, chat-selected / forced server IDs, and tool
allow/deny lists. It approves **all tools on that server** for root plan
mode; a per-tool plan allowlist is deliberately deferred.
## Follow-ups (explicitly out of scope)
- Whether plan-mode subagents should inherit approved external MCP
tools.
- Workspace-local MCP safety model (agent-side `.mcp.json` schema vs. a
coderd-managed workspace MCP config).
## Validation
- `go vet ./coderd/x/chatd/...`
- `go test ./coderd/x/chatd -run 'TestPlan.*|TestMCP.*' -count=1`
- `go test ./coderd/x/chatd -count=1 -timeout 5m` (full chatd suite)
- `make fmt` (no diff)
> Mux opened this PR on Mike's behalf.
GetChildChatsByParentIDs sorted created_at ASC, but the cache
helper appended new children to the end. On refetch the API and
cache agreed on oldest-first, putting the just-created child at
the bottom. Users expect newest first, matching the root-chat
sidebar convention.
- SQL: change child sort to created_at DESC, id DESC.
- Cache: prepend instead of append in addChildToParentInCache
(renamed from appendChildToParentInCache to avoid leaking
position semantics).
- Test: update ordering assertion to expect newest-first.
Refs #24404
GetChats now returns only root chats (parent_chat_id IS NULL).
A new GetChildChatsByParentIDs query fetches children for visible
roots and embeds them in each parent's Children field. The
singular getChat endpoint does the same.
Archive invariant is one-way: parent archived implies child
archived. Parent archive/unarchive cascades via root_chat_id.
Individual child archive is permitted; child unarchive while the
parent is archived is rejected atomically (row lock on child,
re-read parent inside the transaction). Embedded children are
filtered by the caller's archive state so individually-archived
children stay hidden from active-parent views.
Gitsync MarkStale uses GetChatsByWorkspaceIDs directly;
MarkStaleParams.OwnerID removed (dead after the switch).
Frontend: buildChatTree reads from the embedded children field,
WebSocket handlers route child events into the parent's children
array, and archiving a child strips it from the parent cache.
<!--
If you have used AI to produce some or all of this PR, please ensure you have read our [AI Contribution guidelines](https://coder.com/docs/about/contributing/AI_CONTRIBUTING) before submitting.
-->
relates to GRU-18
Adds new database query supporting the Agent Connection Watch we will add.
Adds aggregate PR counts (total, open, merged, closed) from
`chat_diff_statuses` to telemetry snapshots, giving visibility into AI
agent PR outcomes across deployments.
The existing telemetry system reports `Chats`, `ChatMessageSummaries`,
and `ChatModelConfigs`, but had no PR-level data. This adds a
`ChatDiffStatusSummary` field to the `Snapshot` struct with four
all-time counts derived from a single aggregate query.
<details>
<summary>Implementation details</summary>
- New SQL query `GetChatDiffStatusSummary` counts `chat_diff_statuses`
rows with non-NULL `pull_request_state`, grouped by state
(open/merged/closed).
- `ChatDiffStatusSummary` struct added to telemetry `Snapshot`,
collected via a parallel `eg.Go()` block in `createSnapshot()`.
- `dbauthz` wrapper uses `rbac.ResourceSystem` (telemetry-only pattern).
- Test covers both empty state (zero counts) and populated state (mixed
states + NULL-state exclusion).
</details>
> 🤖 Generated by Coder Agents
> This PR was authored by Mux on behalf of Mike.
Introduce Explore mode, a read-only subagent modality for delegated
discovery and code investigation.
## What
Adds a `spawn_explore_agent` tool that creates child chats restricted to
read-only operations. An admin can optionally configure a
deployment-wide
model override so Explore subagents use a model optimized for large
context
or reasoning without changing the root chat's model.
### Backend
- New `ChatModeExplore` enum value (migration 000471).
- `spawn_explore_agent` tool definition with read-only allowlist:
`read_file`, `execute`, `process_output`, `read_skill`,
`read_skill_file`.
Write tools, file editors, and nested subagent spawning are blocked.
- Deployment config storage for the Explore model override
(`agents_chat_explore_model_override` in `site_configs`).
- Model resolution hierarchy: configured override, then current turn
model,
then global default. Silent fallback with warning log when the override
becomes unavailable.
- RBAC: `AsChatd` for daemon reads, `ActionRead` and `ActionUpdate` on
`ResourceDeploymentConfig` for admin API calls.
- Plan mode root chats can use `spawn_explore_agent` for read-only
research,
matching the planning prompt guidance.
- The Explore override config API now reports malformed saved overrides
as
"treated as unset" so admins can clear them explicitly.
### Frontend
- `ExploreModelOverrideSettings` component in admin agent behavior
settings.
Uses `ModelSelector`, handles unavailable model warnings, and supports
explicit Save and Clear actions.
- Malformed saved overrides show a warning and require an explicit Save
to
clear, instead of Clear auto-submitting behind the scenes.
### Tests
- Integration: `TestExploreSubagentIsReadOnly` (full spawn flow, tool
verification, prompt overlay, DB state).
- Unit: tool allowlist tests for explore, plan, and default modes.
- Internal: model override resolution with valid, invalid UUID,
disabled, and
unconfigured override scenarios.
- RBAC: `dbauthz_test.go` for `GetChatExploreModelOverride` and
`UpsertChatExploreModelOverride`.
- API: admin set and clear, malformed stored override reporting,
disabled
model rejection, non-admin denial.
> Mux working on behalf of Mike.
## Summary
- add an enabled chat model config lookup by ID for internal callers
- keep `spawn_agent` unchanged while threading an internal model
override through child subagent chat creation
- extend chatd coverage for inherited bindings, plan mode, and internal
override behavior
## Validation
- `go test ./coderd/x/chatd ./coderd/database/dbauthz`
- `make lint`
Add a `chat_client_type` enum (`ui` | `api`) and `client_type` column to
the `chats` table. The column defaults to `api` for new rows so API
callers don't need to set it explicitly. Existing rows are backfilled to
`ui`.
The field flows through `CreateChatRequest`, `chatd.CreateOptions`,
`InsertChat`, and is returned in the `Chat` response via `db2sdk`.
<details>
<summary>Implementation notes (Coder Agents generated)</summary>
### Changes
**Database migration (000469)**
- New enum `chat_client_type` with values `ui`, `api`.
- New `client_type` column, `NOT NULL DEFAULT 'api'`.
- Backfill: `UPDATE chats SET client_type = 'ui'`.
**SQL query** — `InsertChat` now includes `client_type`.
**SDK** — `ChatClientType` type added; `ClientType` field added to both
`CreateChatRequest` (optional, defaults server-side to `api`) and `Chat`
response.
**Handler** — `postChats` maps the request field (defaulting to `api`)
and passes it through `chatd.CreateOptions`.
**Sub-agent** — Child chats inherit their parent's `client_type`.
**db2sdk** — Maps the database value to the SDK type.
### Decision log
- Default is `api` (not `ui`) so existing API integrations get the
correct value without code changes.
- Backfill sets existing rows to `ui` per requirement.
- Child chats inherit `client_type` from parent rather than defaulting.
</details>
> This PR was authored by Mux on behalf of Mike.
## Summary
Adds support for multiple peer root workspace agents sharing the same
`auth_instance_id`, so AWS, Azure, and GCP instance-identity auth can
issue the correct session token for a selected agent instead of assuming
a
single root agent per instance.
## Problem
When a Terraform template attaches two or more `coder_agent` resources
(with `auth = "aws-instance-identity"`) to a single compute instance,
every agent shares the same cloud instance ID. The existing singular
lookup picks whichever agent was created most recently, silently
ignoring
the others.
## Solution
Introduce an optional pre-auth agent selector (`CODER_AGENT_NAME`) and
make the server-side lookup ambiguity-aware.
**Database layer:**
- `GetWorkspaceAgentsByInstanceID` (`:many`): returns all matching root
agents for an instance ID.
- `GetWorkspaceAgentByInstanceIDAndName` (`:one`): returns the named
root
agent for disambiguation.
**SDK and CLI:**
- `agent_name` field added to AWS, Azure, and GCP request structs
(`omitempty` for backward compatibility).
- `CODER_AGENT_NAME` env var and `--agent-name` flag wired into the
agent
bootstrap before instance-identity auth runs.
**Server handler (`handleAuthInstanceID`):**
- When `agent_name` is present: direct lookup by (instance ID, name).
- When absent: legacy lookup, then resource-scoped ambiguity check.
Returns 409 with available agent names if multiple root agents match.
- Whitespace-only names are trimmed and treated as unspecified.
- Sub-agents remain excluded (`parent_id IS NULL` filter).
**Verification template:**
- `examples/templates/aws-multi-agent/` provisions one EC2 instance with
two agents (`main` and `dev`), both using instance-identity auth with
`CODER_AGENT_NAME` set in the cloud-init user data.
## Backward compatibility
Existing single-agent deployments work unchanged. The `agent_name` field
is optional with `omitempty`, and the unnamed path preserves today's
behavior when only one root agent matches.
> This PR was authored by Mux on behalf of Mike.
## Summary
- add persistent plan mode for chats and the chat-specific plan file
flow
- add structured planning tools such as `ask_user_question` and
`propose_plan`
- keep `write_file` and `edit_files` constrained to the chat-specific
plan file during plan turns
- allow shell exploration in plan mode, including subagents, via
`execute` and `process_output`
- block implementation-oriented, provider-native, MCP, dynamic, and
computer-use tools during plan turns
- update the chat UI, tests, and docs for the new planning flow
Three SQL queries (`GetUserGroupSpendLimit`,
`ResolveUserChatSpendLimit`, `GetUserChatSpendInPeriod`) aggregated chat
spend limits and usage globally across all organizations. A restrictive
group limit in org A would bleed into org B.
## Changes
- Add `organization_id` parameter to all three SQL queries in
`coderd/database/queries/chats.sql`
- When nil UUID is passed, queries fall back to global behavior
(backward compat for HTTP dashboard endpoints)
- When real org ID is passed, limits and spend are scoped to that
organization
- Thread `organizationID` through `ResolveUsageLimitStatus` →
`checkUsageLimit` → all chatd call sites
- Update dbauthz wrappers for new param structs
- HTTP endpoints (`chatCostSummary`, `getMyChatUsageLimitStatus`) pass
`uuid.Nil` with TODO for future org-scoped UI
- Add `TestResolveUsageLimitStatus_OrgScoped` with 5 test cases covering
org isolation, nil-UUID fallback, spend scoping, and user override
priority
Closescoder/internal#1466
> 🤖
Fixes https://github.com/coder/internal/issues/1436
* Adds organization_id to chats with backfill (workspace org → user org membership → default org)
* No support yet for ACLs (follow-up issue)
- Cross-org workspace binding rejected (both in `CreateChatRequest` and in `create_workspace` tool
- Adds `OrganizationAutocomplete` to `AgentCreateForm`
- Docs updated with `organization_id` in chats-api.md
> 🤖 Written by a Coder Agent. Reviewed by many humans and many agents.
---------
Co-authored-by: Mathias Fredriksson <mafredri@gmail.com>
The GetChats SQL query ordered by (updated_at, id) DESC with no
pin_order awareness. A pinned chat with an old updated_at could
land on page 2+ and be invisible in the sidebar's Pinned section.
Add a 4-column ORDER BY: pinned-first flag DESC, negated pin_order
DESC, updated_at DESC, id DESC. The negation trick keeps all sort
columns DESC so the cursor tuple < comparison still works. Update
the after_id cursor clause to match the expanded sort key.
Fix the false handler comment claiming PinChatByID bumps updated_at.
Closes#16332
Previously `coder provisioner jobs list` showed no indication of what a workspace
build job was doing (i.e., start, stop, or delete). This adds
`workspace_build_transition` to the provisioner job metadata, exposed in
both the REST API and CLI. Template and workspace name columns were also
added, both available via `-c`.
```
$ coder provisioner jobs list -c id,type,status,"workspace build transition"
ID TYPE STATUS WORKSPACE BUILD TRANSITION
95f35545-a59f-4900-813d-80b8c8fd7a33 template_version_import succeeded
0a903bbe-cef5-4e72-9e62-f7e7b4dfbb7a workspace_build succeeded start
```
The "By model" and "Pull requests" tables on the PR Insights page
(`/agents/settings/insights`) were side-by-side at `lg` breakpoints, and
the Pull requests table was hard-capped at 20 rows by the backend.
- Replaced `lg:grid-cols-2` with a single-column stacked layout so both
tables span the full content width.
- Removed the `LIMIT 20` from the `GetPRInsightsRecentPRs` SQL query so
all PRs in the selected time range are returned.
- Can add this back if we need it. If we do, we should add a little
subheader above this table to indicate that we're not showing all PRs
within the selected timeframe.
- Added client-side pagination to the Pull requests table using
`PaginationWidgetBase` (page size 10), matching the existing pattern in
`ChatCostSummaryView`.
- Renamed the section heading from "Recent" to "Pull requests" since it
now shows the full set for the time range.
<img width="1481" height="1817" alt="image"
src="https://github.com/user-attachments/assets/0066c42f-4d7b-4cee-b64b-6680848edc68"
/>
> 🤖 PR generated with Coder Agents
When a devcontainer subagent is terraform-managed, the provisioner sets
its directory to the host-side `workspace_folder` path at build time. At
runtime, the agent injection code determines the correct
container-internal
path from `devcontainer read-configuration` and sends it via
`CreateSubAgent`.
However, the `CreateSubAgent` handler only updated `display_apps` for
pre-existing agents, ignoring the `Directory` field. This caused
SSH/terminal
sessions to land in `~` instead of the workspace folder (e.g.
`/workspaces/foo`).
Add `UpdateWorkspaceAgentDirectoryByID` query and call it in the
terraform-managed subagent update path to also persist the directory.
Fixes PLAT-118
<details><summary>Root cause analysis</summary>
Two code paths set the subagent `Directory` field:
1. **Provisioner (build time):** `insertDevcontainerSubagent` in
`provisionerdserver.go`
stores `dc.GetWorkspaceFolder()` — the **host-side** path from the
`coder_devcontainer` Terraform resource (e.g. `/home/coder/project`).
2. **Agent injection (runtime):**
`maybeInjectSubAgentIntoContainerLocked` in
`api.go` reads the devcontainer config and gets the correct
**container-internal**
path (e.g. `/workspaces/project`), then calls `client.Create(ctx,
subAgentConfig)`.
For terraform-managed subagents (those with `req.Id != nil`),
`CreateSubAgent`
in `coderd/agentapi/subagent.go` recognized the pre-existing agent and
entered
the update path — but only called `UpdateWorkspaceAgentDisplayAppsByID`,
discarding the `Directory` field from the request. The agent kept the
stale
host-side path, which doesn't exist inside the container, causing
`expandPathToAbs` to fall back to `~`.
</details>
> [!NOTE]
> Generated by Coder Agents
Add the five REST endpoints for managing user secrets, SDK client
methods, and handler tests.
Endpoints:
- `POST /api/v2/users/{user}/secrets`
- `GET /api/v2/users/{user}/secrets`
- `GET /api/v2/users/{user}/secrets/{name}`
- `PATCH /api/v2/users/{user}/secrets/{name}`
- `DELETE /api/v2/users/{user}/secrets/{name}`
Routes are registered under the existing `/{user}` group with
`ExtractUserParam`. The delete query was changed from `:exec` to
`:execrows` so the handler can distinguish "not found" from success
(DELETE with `:exec` silently returns nil for zero affected rows).
Adds `coder exp chat context add` and `coder exp chat context clear`
commands that run inside a workspace to manage chat context files via
the agent token.
`add` reads instruction and skill files from a directory (defaulting to
cwd) and inserts them as context-file messages into an active chat.
Multiple calls are additive — `instructionFromContextFiles` already
accumulates all context-file parts across messages.
`clear` soft-deletes all context-file messages, causing
`contextFileAgentID()` to return `!found` on the next turn, which
triggers `needsInstructionPersist=true` and re-fetches defaults from the
agent.
Both commands auto-detect the target chat via `CODER_CHAT_ID` (already
set by `agentproc` on chat-spawned processes), or fall back to
single-active-chat resolution for the agent. The `--chat` flag overrides
both.
Also adds sub-agent context inheritance: `createChildSubagentChat` now
copies parent context-file messages to child chats at spawn time, so
delegated sub-agents share the same instruction context without
independently re-fetching from the workspace agent.
<details><summary>Implementation details</summary>
**New files:**
- `cli/exp_chat.go` — CLI command tree under `coder exp chat context`
**Modified files:**
- `agent/agentcontextconfig/api.go` — `ConfigFromDir()` reads context
from an arbitrary directory without env vars
- `codersdk/agentsdk/agentsdk.go` — `AddChatContext`/`ClearChatContext`
SDK methods
- `coderd/workspaceagents.go` — POST/DELETE handlers on
`/workspaceagents/me/chat-context`
- `coderd/coderd.go` — Route registration
- `coderd/database/queries/chats.sql` — `GetActiveChatsByAgentID`,
`SoftDeleteContextFileMessages`
- `coderd/database/dbauthz/dbauthz.go` — RBAC implementations for new
queries
- `coderd/x/chatd/subagent.go` — `copyParentContextFiles` for sub-agent
inheritance
- `cli/root.go` — Register `chatCommand()` in `AGPLExperimental()`
**Auth pattern:** Uses `AgentAuth` (same as `coder external-auth`) —
agent token via `CODER_AGENT_TOKEN` + `CODER_AGENT_URL` env vars.
</details>
> 🤖 Generated by Coder Agents
---------
Co-authored-by: Michael Suchacz <203725896+ibetitsmike@users.noreply.github.com>
## Summary
Adds `credential_kind` and `credential_hint` columns to
`aibridge_interceptions` to record how each LLM request was
authenticated and provide a masked credential identifier for audit
purposes.
This enables admins to distinguish between centralized API keys,
personal API keys, and subscription-based credentials in the
interceptions audit log.
## Changes
- New migration adding `credential_kind`and `credential_hint` to
`aibridge_interceptions`
- Updated `InsertAIBridgeInterception` query and proto definition to
carry the new fields
- Wired proto fields through `translator.go` and `aibridgedserver.go` to
the database
Depends on https://github.com/coder/aibridge/pull/239
Adds client-executed dynamic tools to the chat API. Dynamic tools are
declared by the client at chat creation time, presented to the LLM
alongside built-in tools, but executed by the client rather than chatd.
This enables external systems (Slack bots, IDE extensions, Discord bots,
CI/CD integrations) to plug custom tools into the LLM chat loop without
modifying chatd's built-in tool set.
Modeled after OpenAI's Assistants API: the chat pauses with
`requires_action` status when the LLM calls a dynamic tool, the client
POSTs results back via `POST /chats/{id}/tool-results`, and the chat
resumes.
See [this example](https://github.com/coder/coder-slackbot-poc) as a
reference for how this is used. It's highly-configurable, which would
enable creating chats from webhooks, periodically polling, or running as
a Slackbot.
<details>
<summary>Design context</summary>
### Architecture
The chatloop **exits** when it encounters dynamic tools and
**re-enters** when results arrive. No blocking channels, no pubsub for
tool results, no in-memory registry. The DB is the only coordination
mechanism.
```
Phase 1 (chatloop):
LLM response → execute built-in tools only →
Persist(assistant + built-in results) →
status = requires_action → chatloop exits
Phase 2 (POST /tool-results):
Persist(dynamic tool results) →
status = pending → wakeCh → chatloop re-enters
```
### Validation (POST /tool-results)
1. Chat status must be `requires_action` (409 if not)
2. Read chat's `dynamic_tools` → set of dynamic tool names
3. Read last assistant message → extract tool-call parts matching
dynamic tool names
4. Submitted tool_call_ids must match exactly (400 for missing/extra)
5. Persist tool-result message parts, set status to `pending`, signal
wake
### Idempotency
Tool call IDs scoped per LLM step. State machine (`requires_action` →
`pending`) is the guard. First POST wins, subsequent get 409.
### Mixed tool calls
When the LLM calls both built-in and dynamic tools in one step, built-in
tools execute immediately. Their results are persisted in phase 1.
Dynamic tool results arrive via POST in phase 2. The LLM sees all
results when the chatloop resumes.
</details>
> 🤖 Generated by Coder Agents
Adds telemetry collection for the agents chat system (`/agents`) to the
existing telemetry snapshot pipeline.
Three new snapshot fields:
- **`Chats`** — per-chat metadata (id, owner, status, mode,
workspace_id, root_chat_id, has_parent, archived, model config)
collected time-windowed via `createdAfter`
- **`ChatMessageSummaries`** — per-chat aggregated message metrics
(counts by role, token sums by type, cost, runtime, model count,
compression count) collected time-windowed
- **`ChatModelConfigs`** — model configuration metadata (provider,
model, context limit, enabled, default) collected as full dump
No PII is included — titles, message content, and URLs are excluded at
the SQL level. Only structural metadata flows through telemetry.
<details><summary>Implementation plan</summary>
### SQL Queries (`coderd/database/queries/chats.sql`)
- `GetChatsCreatedAfter` — time-windowed chat metadata
- `GetChatMessageSummariesPerChat` — per-chat message aggregates via
`GROUP BY`
- `GetChatModelConfigsForTelemetry` — full dump of model configs
### Telemetry (`coderd/telemetry/telemetry.go`)
- `Chat`, `ChatMessageSummary`, `ChatModelConfig` structs
- `ConvertChat`, `ConvertChatMessageSummary`, `ConvertChatModelConfig`
conversion functions
- Three `eg.Go()` blocks in `createSnapshot()` following the existing
collection pattern
### Authorization (`coderd/database/dbauthz/dbauthz.go`)
- System-only access for all three queries via `rbac.ResourceSystem`
### Tests
- `TestChatsTelemetry` in `coderd/telemetry/telemetry_test.go` — creates
chats (root + child), messages with token/cost data, model configs;
verifies all snapshot fields
- dbauthz test entries for all three queries in
`coderd/database/dbauthz/dbauthz_test.go`
</details>
> 🤖 Generated by Coder Agents
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.
Update queries as prep work for user secrets API development:
- Switch all lookups and mutations from ID-based to user_id + name
- Split list query into metadata-only (for API responses) and
with-values (for provisioner/agent)
- Add partial update support using CASE WHEN pattern for write-only
value fields
- Include value_key_id in create for dbcrypt encryption support
- Update dbauthz wrappers and remove stale methods from dbmetrics
## Summary
Replaces N per-chat heartbeat goroutines with a single centralized
heartbeat loop that issues one `UPDATE` per 30s interval for all running
chats on a worker.
## Problem
Each running chat spawned a dedicated goroutine that issued an
individual `UPDATE chats SET heartbeat_at = NOW() WHERE id = $1 AND
worker_id = $2 AND status = 'running'` query every 30 seconds. At 10,000
concurrent chats this produces **~333 DB queries/second** just for
heartbeats, plus ~333 `ActivityBumpWorkspace` CTE queries/second from
`trackWorkspaceUsage`.
## Solution
New `UpdateChatHeartbeats` (plural) SQL query replaces the old singular
`UpdateChatHeartbeat`:
```sql
UPDATE chats
SET heartbeat_at = @now::timestamptz
WHERE worker_id = @worker_id::uuid
AND status = 'running'::chat_status
RETURNING id;
```
A single `heartbeatLoop` goroutine on the `Server`:
1. Ticks every `chatHeartbeatInterval` (30s)
2. Issues one batch UPDATE for all registered chats
3. Detects stolen/completed chats via set-difference (equivalent of old
`rows == 0`)
4. Calls `trackWorkspaceUsage` for surviving chats
`processChat` registers an entry in the heartbeat registry instead of
spawning a goroutine.
## Impact
| Metric | Before (10K chats) | After (10K chats) |
|---|---|---|
| Heartbeat queries/sec | ~333 | ~0.03 (1 per 30s per replica) |
| Heartbeat goroutines | 10,000 | 1 |
| Self-interrupt detection | Per-chat `rows==0` | Batch set-difference |
---
> 🤖 Generated by Coder Agents
<details><summary>Implementation notes</summary>
- Uses `@now` parameter instead of `NOW()` so tests with `quartz.Mock`
can control timestamps.
- `heartbeatEntry` stores `context.CancelCauseFunc` + workspace state
for the centralized loop.
- `recoverStaleChats` is unaffected — it reads `heartbeat_at` which is
still updated.
- The old singular `UpdateChatHeartbeat` is removed entirely.
- `dbauthz` wrapper uses system-level `rbac.ResourceChat` authorization
(same pattern as `AcquireChats`).
</details>
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.
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
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 a nullable `value_key_id` column to the `user_secrets` table with a
foreign key to `dbcrypt_keys`. This is the column dbcrypt uses to track
which encryption key encrypted a given secret's value. This is required
for encryption of user secret values.
The column was missing from the original migration (000357).
## 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 ._
Previously, `CreateChat` inserted the `chats` row with the DB default
status (`waiting`), then updated it to `pending` in the same transaction
via `setChatPendingWithStore`. This wasted two extra queries per chat
creation (`GetChatByID` + `UpdateChatStatus`) and rewrote the same row
immediately after inserting it.
Now `CreateChat` passes the status directly to `InsertChat`, so the row
is written once in its final create-time state. The
`setChatPendingWithStore` helper is removed entirely. `InsertChat` now
requires an explicit `status` parameter at all callsites instead of
relying on a DB column default.
## Motivation
On an experimental branch we're trialing firing all chatd notifications
from plpgsql triggers. The old two-step insert made that awkward: in an
`AFTER INSERT` trigger, `NEW` only contained the insert-time row
(`waiting`), not the final committed state (`pending`). To emit the
correct event payload the trigger had to be deferred and re-read the row
from `chats` at commit time.
With this change, `NEW` already contains the correct row to publish — no
deferred trigger, no extra `SELECT`, simpler and cheaper trigger logic.
That said, this seems like a worthwhile change regardless of the trigger
experiment: writing the final row state once removes unnecessary DB work
on every chat creation and makes the create path easier to reason about.
Unarchiving a root chat now restores descendant chats in the database
and emits lifecycle events for every affected chat so passive sessions
converge without a full refetch.
This keeps archive and unarchive symmetric at both the data and
watch-stream layers by returning the affected chat family from the
database, using those post-update rows for chatd pubsub fanout, and
covering descendant lifecycle delivery with a watch-level regression
test.
Closes#23666
_Disclaimer: produced using Claude Opus 4.6, reviewed by me, and
validated against Dogfood dataset._
The `ListAIBridgeSessions` query materialized and aggregated all
matching interceptions before paginating, then ran expensive
token/prompt lookups across the full dataset. For a page of 25 sessions
against ~200k interceptions (our dogfood dataset), this meant:
- Three CTEs scanning all rows (filtered_interceptions, session_tokens,
session_root)
- ARRAY_AGG(fi.id) collecting every interception ID per session
- Lateral prompt lookup via ANY(array_of_all_ids) running for every
session, not just the page
- ~90MB of disk sorts and JIT compilation kicking in
The improvement is to restructure to paginate first and enrich after: a
single CTE groups interceptions into sessions with only cheap aggregates
(MIN, MAX, COUNT), applies cursor pagination and LIMIT, then lateral
joins fetch metadata, tokens, and prompts for just the ~25-row page.
Measured against 220k interceptions / 160k sessions:
| Metric | Before | After |
|--------------------|--------|-------|
| Execution time | 1800ms | 185ms |
| Shared buffer hits | 737k | 2.6k |
| Disk sort spill | 86MB | 16MB |
| Lateral loops | 160k | 25 |
https://grafana.dev.coder.com/goto/fbODPGtvR?orgId=1 the results are
identical, just _much_ faster.
---
Also includes some additional tests which I added prior to refactoring
the query to ensure no regressions on edge-cases.
---------
Signed-off-by: Danny Kopping <danny@coder.com>
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>
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
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.
Jeremy Ruppel