* feat: add PreferDevelopmentBackends setting, expose isMeta/isDevelopment in API
- Add PreferDevelopmentBackends config field, CLI flag, runtime setting
- Add IsDevelopment() method to GalleryBackend
- Use AvailableBackendsUnfiltered in UI API to show all backends
- Expose isMeta, isDevelopment, preferDevelopmentBackends in backend API response
* feat: upgrade banner with Upgrade All button, detect pre-existing backends
- Add upgrade banner on Backends page showing count and Upgrade All button
- Fix upgrade detection for backends installed before version tracking:
flag as upgradeable when gallery has a version but installed has none
- Fix OCI digest check to flag backends with no stored digest as upgradeable
* feat: add backend versioning data model foundation
Add Version, URI, and Digest fields to BackendMetadata for tracking
installed backend versions and enabling upgrade detection. Add Version
field to GalleryBackend. Add UpgradeAvailable/AvailableVersion fields
to SystemBackend. Implement GetImageDigest() for lightweight OCI digest
lookups via remote.Head. Record version, URI, and digest at install time
in InstallBackend() and propagate version through meta backends.
* feat: add backend upgrade detection and execution logic
Add CheckBackendUpgrades() to compare installed backend versions/digests
against gallery entries, and UpgradeBackend() to perform atomic upgrades
with backup-based rollback on failure. Includes Agent A's data model
changes (Version/URI/Digest fields, GetImageDigest).
* feat: add AutoUpgradeBackends config and runtime settings
Add configuration and runtime settings for backend auto-upgrade:
- RuntimeSettings field for dynamic config via API/JSON
- ApplicationConfig field, option func, and roundtrip conversion
- CLI flag with LOCALAI_AUTO_UPGRADE_BACKENDS env var
- Config file watcher support for runtime_settings.json
- Tests for ToRuntimeSettings, ApplyRuntimeSettings, and roundtrip
* feat(ui): add backend version display and upgrade support
- Add upgrade check/trigger API endpoints to config and api module
- Backends page: version badge, upgrade indicator, upgrade button
- Manage page: version in metadata, context-aware upgrade/reinstall button
- Settings page: auto-upgrade backends toggle
* feat: add upgrade checker service, API endpoints, and CLI command
- UpgradeChecker background service: checks every 6h, auto-upgrades when enabled
- API endpoints: GET /backends/upgrades, POST /backends/upgrades/check, POST /backends/upgrade/:name
- CLI: `localai backends upgrade` command, version display in `backends list`
- BackendManager interface: add UpgradeBackend and CheckUpgrades methods
- Wire upgrade op through GalleryService backend handler
- Distributed mode: fan-out upgrade to worker nodes via NATS
* fix: use advisory lock for upgrade checker in distributed mode
In distributed mode with multiple frontend instances, use PostgreSQL
advisory lock (KeyBackendUpgradeCheck) so only one instance runs
periodic upgrade checks and auto-upgrades. Prevents duplicate
upgrade operations across replicas.
Standalone mode is unchanged (simple ticker loop).
* test: add e2e tests for backend upgrade API
- Test GET /api/backends/upgrades returns 200 (even with no upgrade checker)
- Test POST /api/backends/upgrade/:name accepts request and returns job ID
- Test full upgrade flow: trigger upgrade via API, wait for job completion,
verify run.sh updated to v2 and metadata.json has version 2.0.0
- Test POST /api/backends/upgrades/check returns 200
- Fix nil check for applicationInstance in upgrade API routes
* feat: add toggle mechanism to enable/disable models from loading on demand
Implements #9303 - Adds ability to disable models from being auto-loaded
while keeping them in the collection.
Backend changes:
- Add Disabled field to ModelConfig struct with IsDisabled() getter
- New ToggleModelEndpoint handler (PUT /models/toggle/:name/:action)
- Request middleware returns 403 when disabled model is requested
- Capabilities endpoint exposes disabled status
Frontend changes:
- Toggle switch in System > Models table Actions column
- Visual indicators: dimmed row, red Disabled badge, muted icons
- Tooltip describes toggle function on hover
- Loading state while API call is in progress
* fix: remove extra closing brace causing syntax error in request middleware
* refactor: reorder Actions column - Stop button before toggle switch
* refactor: migrate from toggle to toggle-state per PR review feedback
When TASK_RESPONSE_TYPE_OAI_CHAT is used, the first streaming token
produces a JSON array with two elements: a role-init chunk and the
actual content chunk. The grpc-server loop called attach_chat_deltas
for both elements with the same raw_result pointer, stamping the first
token's ChatDelta.Content on both replies. The Go side accumulated both,
emitting the first content token twice to SSE clients.
Fix: in the array iteration loops in PredictStream, detect role-init
elements (delta has "role" key) and skip attach_chat_deltas for them.
Only content/reasoning elements get chat deltas attached.
Reasoning models are unaffected because their first token goes into
reasoning_content, not content.
The Go-side incremental JSON parser was emitting the same tool call on
every streaming token because it lacked the len > lastEmittedCount guard
that the XML parser had. On top of that, the post-streaming default:
case re-emitted all tool calls from index 0, duplicating everything.
This produced duplicate delta.tool_calls events causing clients to
accumulate arguments as "{args}{args}" — invalid JSON.
Fixes:
- JSON incremental parser: add len(jsonResults) > lastEmittedCount guard
and loop from lastEmittedCount (matching the XML parser pattern)
- Post-streaming default: case: skip i < lastEmittedCount entries that
were already emitted during streaming
- JSON parser: use blocking channel send (matching XML parser behavior)
When clients like Nextcloud or Home Assistant send requests with tools
to thinking models (e.g. Gemma 4 with <|channel>thought tags), the
response was empty despite the backend producing valid content.
Root cause: the C++ autoparser puts clean content in both the raw
Response and ChatDeltas. The Go-side PrependThinkingTokenIfNeeded
then prepends the thinking start token to the already-clean content,
causing ExtractReasoning to classify the entire response as unclosed
reasoning. This made cbRawResult empty, triggering a retry loop that
never succeeds.
Two fixes:
- inference.go: check ChatDeltas for content/tool_calls regardless of
whether Response is empty, so skipCallerRetry fires correctly
- chat.go: when ChatDeltas have content but no tool calls, use that
content directly instead of falling back to the empty cbRawResult
This changeset makes visible when files are being staged, so users are
aware that the model "isn't ready yet" for requests.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): Add dynamic model editor with autocomplete
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(docs): Add link to longformat installation video
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
We were not checking against the api keys when db == nil.
This commit also cleanups now unused middleware
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
