Backend now calculates and returns the total number of scheduled execution runs in the next hour and 24 hours, not just unique schedules. The frontend displays these new metrics in the diagnostics admin panel. The OpenAPI schema is updated to reflect the new fields.
Introduces backend and frontend support for stopping all long-running executions and cleaning up all stuck queued executions via new admin endpoints. Updates diagnostics logic to ensure both cancel signals and DB status updates are performed, adds corresponding API routes, and enhances the admin UI to expose these bulk actions. Also updates the sidebar icon for diagnostics.
Extract error messages from the stats JSON field in failed executions details. Update the admin diagnostics route to always count the actual number of failed executions within the specified time window, ensuring accurate pagination.
Introduces backend endpoints and models for schedule diagnostics, including orphaned schedule detection, listing, and bulk cleanup. Updates the frontend to display schedule health metrics and a new schedules table with management actions. OpenAPI spec is updated to document the new endpoints and models.
Add extensive diagnostic capabilities for on-call engineers to monitor and manage execution health.
Backend Enhancements:
- Add 18 diagnostic metrics covering failures, orphaned executions, stuck queued, throughput, and queue health
- Implement orphaned execution detection (>24h old, not in executor)
- Add stuck queued detection (QUEUED >1h, never started)
- Add long-running execution detection (RUNNING >24h)
- Monitor both execution and cancel RabbitMQ queues
- Track failure rates (1h, 24h) and execution throughput metrics
New Backend Endpoints (15 total):
- GET /admin/diagnostics/executions/orphaned - List orphaned executions
- GET /admin/diagnostics/executions/stuck-queued - List stuck queued executions
- GET /admin/diagnostics/executions/long-running - List long-running executions
- GET /admin/diagnostics/executions/failed - List failed executions with error messages
- POST /admin/diagnostics/executions/cleanup-all-orphaned - Cleanup all orphaned (operates on entire dataset)
- POST /admin/diagnostics/executions/requeue - Requeue single stuck execution
- POST /admin/diagnostics/executions/requeue-bulk - Requeue selected executions
- POST /admin/diagnostics/executions/requeue-all-stuck - Requeue all stuck queued (operates on entire dataset)
Execution Management:
- Dual-mode stop: Active executions (cancel signals) vs orphaned (direct DB cleanup)
- Intelligent Stop All: Auto-splits active/orphaned, executes in parallel
- Requeue functionality for stuck QUEUED executions with credit cost warnings
- Stop sends cancel signals to RabbitMQ for graceful termination
- Cleanup orphaned updates DB directly without cancel signals
- ALL endpoints operate on entire datasets (not limited to pagination)
Frontend Enhancements:
- 5-tab filtering interface: All, Orphaned, Stuck Queued, Long-Running, Failed
- Clickable alert cards (🟠🔴🟡) automatically switch to relevant tabs
- Tab badges show live counts from diagnostics metrics
- Age column displays execution duration (e.g., "245d 12h")
- Orange row highlighting for orphaned executions (>24h old)
- Error message column for failed executions with hover tooltips
- Click-to-copy for execution IDs and user IDs with visual feedback
- Status badge colors match library view (blue=RUNNING, yellow=QUEUED, red=FAILED)
Tab-Specific Actions:
- Stuck Queued: Cleanup All OR Requeue All buttons with cost warnings
- Stuck Queued per-row: 🟠 Cleanup OR 🔵 Requeue buttons
- Orphaned: Cleanup All (operates on ALL orphaned)
- Long-Running: Stop All (sends cancel signals)
- Failed: View-only with error details
- All: Stop All (intelligent split of active/orphaned)
Alert Cards:
- 🟠 Orphaned: Shows count with RUNNING/QUEUED breakdown, click to view
- 🔴 Failed (24h): Shows count with hourly rate, click to view
- 🟡 Long-Running: Shows count with oldest execution age, click to view
Updated Diagnostic Info Card:
- Color-coded explanations for each execution type
- When to cleanup vs requeue vs stop
- Credit cost implications clearly documented
- Queue health thresholds explained
Provides ~70% coverage of on-call guide requirements for troubleshooting execution issues, orphaned database records, and system health monitoring.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
### Changes 🏗️
- Prevent removing progress of user onboarding tasks by merging arrays
on the backend instead of replacing them
- New endpoint for onboarding reset
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Tasks are not being reset
- [x] `/onboarding/reset` works
- #11273
- Bump `apscheduler` to v3.11.1 which contains a fix for the issue
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] "It's a rather ugly solution but the test proves that it works."
~the maintainer
- [x] CI passes
<!-- Clearly explain the need for these changes: -->
This PR addresses the need for consistent error handling across all
blocks in the AutoGPT platform. Previously, each block had to manually
define an `error` field in their output schema, leading to code
duplication and potential inconsistencies. Some blocks might forget to
include the error field, making error handling unpredictable.
### Changes 🏗️
<!-- Concisely describe all of the changes made in this pull request:
-->
- **Created `BlockSchemaOutput` base class**: New base class that
extends `BlockSchema` with a standardized `error` field
- **Created `BlockSchemaInput` base class**: Added for consistency and
future extensibility
- **Updated 140+ block implementations**: Changed all block `Output`
classes from `class Output(BlockSchema):` to `class
Output(BlockSchemaOutput):`
- **Removed manual error field definitions**: Eliminated hundreds of
duplicate `error: str = SchemaField(...)` definitions
- **Updated type annotations**: Changed `Block[BlockSchema,
BlockSchema]` to `Block[BlockSchemaInput, BlockSchemaOutput]` throughout
the codebase
- **Fixed imports**: Added `BlockSchemaInput` and `BlockSchemaOutput`
imports to all relevant files
- **Maintained backward compatibility**: Updated `EmptySchema` to
inherit from `BlockSchemaOutput`
**Key Benefits:**
- Consistent error handling across all blocks
- Reduced code duplication (removed ~200 lines of repetitive error field
definitions)
- Type safety improvements with distinct input/output schema types
- Blocks can still override error field with more specific descriptions
when needed
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] Verified `poetry run format` passes (all linting, formatting, and
type checking)
- [x] Tested block instantiation works correctly (MediaDurationBlock,
UnrealTextToSpeechBlock)
- [x] Confirmed error fields are automatically present in all updated
blocks
- [x] Verified block loading system works (successfully loads 353+
blocks)
- [x] Tested backward compatibility with EmptySchema
- [x] Confirmed blocks can still override error field with custom
descriptions
- [x] Validated core schema inheritance chain works correctly
#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)
*Note: No configuration changes were needed for this refactoring.*
🤖 Generated with [Claude Code](https://claude.ai/code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Lluis Agusti <hi@llu.lu>
Co-authored-by: Ubbe <hi@ubbe.dev>
### Changes 🏗️
- Increased `max_field_size` in `aiohttp.ClientSession` to 16KB to
handle servers with large headers (e.g., long CSP headers).
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] Add unit test that checks it can now parse headers over 8k size
---------
Co-authored-by: seer-by-sentry[bot] <157164994+seer-by-sentry[bot]@users.noreply.github.com>
Co-authored-by: Swifty <craigswift13@gmail.com>
Co-authored-by: Ubbe <hi@ubbe.dev>
### Changes 🏗️
- Added validation to ensure that the `summary` and `final_summary`
returned by the LLM are strings.
- Raises a `ValueError` if the LLM returns a list or other non-string
type, providing a descriptive error message to aid debugging.
Fixes
[AUTOGPT-SERVER-6M4](https://sentry.io/organizations/significant-gravitas/issues/6978480131/).
The issue was that: LLM returned list of strings instead of single
string summary, causing `_combine_summaries` to fail on `join`.
This fix was generated by Seer in Sentry, triggered by Craig Swift. 👁️
Run ID: 2230933
Not quite right? [Click here to continue debugging with
Seer.](https://sentry.io/organizations/significant-gravitas/issues/6978480131/?seerDrawer=true)
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] Added a unit test to verify that a ValueError is raised when the
LLM returns a list instead of a string for summary or final_summary.
---------
Co-authored-by: seer-by-sentry[bot] <157164994+seer-by-sentry[bot]@users.noreply.github.com>
Co-authored-by: Swifty <craigswift13@gmail.com>
Marketplace sort by functionality was not working on the frontend. This
PR fixes it
### Changes 🏗️
- Add type hints for sort by
- Fix marketplace sort by drop downs
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] tested locally
### Changes 🏗️
Enhanced SQL query security in the store search functionality by
implementing proper parameterization to prevent SQL injection
vulnerabilities.
**Security Improvements:**
- Replaced string interpolation with PostgreSQL positional parameters
(`$1`, `$2`, etc.) for all user inputs
- Added ORDER BY whitelist validation to prevent injection via
`sorted_by` parameter
- Parameterized search term, creators array, category, and pagination
values
- Fixed variable naming conflict (`sql_where_clause` vs `where_clause`)
**Testing:**
- Added 4 comprehensive tests validating SQL injection prevention across
different attack vectors
- Tests verify that malicious input in search queries, filters, sorting,
and categories are safely handled
- All 10 tests in db_test.py pass successfully
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] All existing tests pass (10/10 tests passing)
- [x] New security tests validate SQL injection prevention
- [x] Verified parameterized queries handle malicious input safely
- [x] Code formatting passes (`poetry run format`)
#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)
*Note: No configuration changes required for this security fix*
## Summary
Fix critical issue where pre-execution permission validation broke
execution of graphs that reference older versions of sub-graphs.
## Problem
The `validate_graph_execution_permissions` function was checking for the
specific version of a graph in the user's library. This caused failures
when:
1. A parent graph references an older version of a sub-graph
2. The user updates the sub-graph to a newer version
3. The older version is no longer in their library
4. Execution of the parent graph fails with `GraphNotInLibraryError`
## Root Cause
In `backend/executor/utils.py` line 523, the function was checking for
the exact version, but sub-graphs legitimately reference older versions
that may no longer be in the library.
## Solution
### 1. Remove Version-Specific Check (backend/executor/utils.py)
- Remove `graph_version=graph.version` parameter from validation call
- Add explanatory comment about version-agnostic behavior
- Now only checks that the graph ID exists in user's library (any
version)
### 2. Enhance Documentation (backend/data/graph.py)
- Update function docstring to explain version-agnostic behavior
- Document that `None` (now default) allows execution of any version
- Clarify this is important for sub-graph version compatibility
## Technical Details
The `validate_graph_execution_permissions` function was already designed
to handle version-agnostic checks when `graph_version=None`. By omitting
the version parameter, we skip the version check and only verify:
- Graph exists in user's library
- Graph is not deleted/archived
- User has execution permissions
## Impact
- ✅ Parent graphs can execute even when they reference older sub-graph
versions
- ✅ Sub-graph updates don't break existing parent graphs
- ✅ Maintains security: still checks library membership and permissions
- ✅ No breaking changes: version-specific validation still available
when needed
## Example Scenario Fixed
1. User creates parent graph that uses sub-graph v1
2. User updates sub-graph to v2 (v1 removed from library)
3. Parent graph still references sub-graph v1
4. **Before**: Execution fails with `GraphNotInLibraryError`
5. **After**: Execution succeeds (version-agnostic permission check)
## Testing
- [x] Code formatting and linting passes
- [x] Type checking passes
- [x] No breaking changes to existing functionality
- [x] Security still maintained through library membership checks
## Files Changed
- `backend/executor/utils.py`: Remove version-specific permission check
- `backend/data/graph.py`: Enhanced documentation for version-agnostic
behavior
Closes #[issue-number-if-applicable]
Co-authored-by: Claude <noreply@anthropic.com>
📨 Fix: Handle Oversized Notification Emails
Summary
This PR adds logic to detect and handle oversized notification emails
exceeding Postmark’s 5 MB limit. Instead of retrying indefinitely, the
system now sends a lightweight summary email with key stats and a
dashboard link.
Changes
Added size check in EmailSender.send_templated()
Sends summary email when payload > ~4.5 MB
Prevents infinite retries and queue clogging
Added logs for oversized detection
Fixes#11119
---------
Co-authored-by: Nicholas Tindle <nicholas.tindle@agpt.co>
Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
- Resolves#11251
This fixes all the warnings mentioned in #11251, reducing noise and
making our logs and error alerts more useful :)
### Changes 🏗️
- Remove "Block {block_name} has multiple credential inputs" warning
(not actually an issue)
- Rename `json` attribute of `MainCodeExecutionResult` to `json_data`;
retain serialized name through a field alias
- Replace `Path(regex=...)` with `Path(pattern=...)` in
`get_shared_execution` endpoint parameter config
- Change Uvicorn's WebSocket module to new Sans-I/O implementation for
WS server
- Disable Uvicorn's WebSocket module for REST server
- Remove deprecated `enable_cleanup_closed=True` argument in
`CloudStorageHandler` implementation
- Replace Prisma transaction timeout `int` argument with a `timedelta`
value
- Update Sentry SDK to latest version (v2.42.1)
- Broaden filter for cleanup warnings from indirect dependency `litellm`
- Fix handling of `MissingConfigError` in REST server endpoints
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- Check that the warnings are actually gone
- [x] Deploy to dev environment and run a graph; check for any warnings
- Test WebSocket server
- [x] Run an agent in the Builder; make sure real-time execution updates
still work
### Changes 🏗️
- Modifies the LLM block to extract the actual response from the
dictionary returned by the LLM, instead of yielding the entire
dictionary. This addresses
[AUTOGPT-SERVER-6EY](https://sentry.io/organizations/significant-gravitas/issues/6950850822/).
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] After applying the fix, I ran the agent that triggered the Sentry
error and confirmed that it now completes successfully without errors.
---------
Co-authored-by: seer-by-sentry[bot] <157164994+seer-by-sentry[bot]@users.noreply.github.com>
Co-authored-by: Swifty <craigswift13@gmail.com>
Fixes
[AUTOGPT-SERVER-6KZ](https://sentry.io/organizations/significant-gravitas/issues/6976926125/).
The issue was that: Redirect handling strips the URL scheme, causing
subsequent requests to fail validation and hit a 404.
- Ensures the hostname in the URL is properly IDNA-encoded after
validation.
- Reconstructs the netloc with the encoded hostname and preserves the
port if it exists.
This fix was generated by Seer in Sentry, triggered by Craig Swift. 👁️
Run ID: 2204774
Not quite right? [Click here to continue debugging with
Seer.](https://sentry.io/organizations/significant-gravitas/issues/6976926125/?seerDrawer=true)
### Changes 🏗️
**backend/util/request.py:**
- Fixed URL validation to properly preserve port numbers when
reconstructing netloc
- Ensures IDNA-encoded hostname is combined with port (if present)
before URL reconstruction
**Test Results:**
- ✅ Tested request to https://www.target.com/ (original failing URL from
Sentry issue)
- ✅ Status: 200, Content retrieved successfully (339,846 bytes)
- ✅ Port preservation verified for URLs with explicit ports
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Tested request to https://www.target.com/ (original failing URL)
- [x] Verified status code 200 and successful content retrieval
- [x] Verified port preservation in URL validation
<details>
<summary>Example test plan</summary>
- [ ] Create from scratch and execute an agent with at least 3 blocks
- [ ] Import an agent from file upload, and confirm it executes
correctly
- [ ] Upload agent to marketplace
- [ ] Import an agent from marketplace and confirm it executes correctly
- [ ] Edit an agent from monitor, and confirm it executes correctly
</details>
#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)
<details>
<summary>Examples of configuration changes</summary>
- Changing ports
- Adding new services that need to communicate with each other
- Secrets or environment variable changes
- New or infrastructure changes such as databases
</details>
Co-authored-by: seer-by-sentry[bot] <157164994+seer-by-sentry[bot]@users.noreply.github.com>
Co-authored-by: Swifty <craigswift13@gmail.com>
Removes CLAUDE_3_5_SONNET and CLAUDE_3_5_HAIKU from LlmModel enum, model
metadata, and cost configuration since they are deprecated
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Verify the models are gone from the llm blocks
Categories and Creators where not sanitized in the full text search
- apply sanitization to categories and creators
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] run tests to check it still works
Categories and Creators where not sanitized in the full text search
### Changes 🏗️
- apply sanitization to categories and creators
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] run tests to check it still works
Added error output pins to all Firecrawl blocks as standard on the
AutoGPT platform. The base block execution code already handles error
yielding, so no try-catch logic was needed.
- FirecrawlScrapeBlock: Added error output pin for scrape failures
- FirecrawlCrawlBlock: Added error output pin for crawl failures
- FirecrawlExtractBlock: Added error output pin for extraction failures
- FirecrawlMapBlock: Added error output pin for map failures
- FirecrawlSearchBlock: Added error output pin for search failures
Resolves#11253
<!-- Clearly explain the need for these changes: -->
### Changes 🏗️
<!-- Concisely describe all of the changes made in this pull request:
-->
### Checklist 📋
#### For code changes:
- [ ] I have clearly listed my changes in the PR description
- [ ] I have made a test plan
- [ ] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [ ] ...
<details>
<summary>Example test plan</summary>
- [ ] Create from scratch and execute an agent with at least 3 blocks
- [ ] Import an agent from file upload, and confirm it executes
correctly
- [ ] Upload agent to marketplace
- [ ] Import an agent from marketplace and confirm it executes correctly
- [ ] Edit an agent from monitor, and confirm it executes correctly
</details>
#### For configuration changes:
- [ ] `.env.default` is updated or already compatible with my changes
- [ ] `docker-compose.yml` is updated or already compatible with my
changes
- [ ] I have included a list of my configuration changes in the PR
description (under **Changes**)
<details>
<summary>Examples of configuration changes</summary>
- Changing ports
- Adding new services that need to communicate with each other
- Secrets or environment variable changes
- New or infrastructure changes such as databases
</details>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Toran Bruce Richards <Torantulino@users.noreply.github.com>
We're currently seeing errors in the `DatabaseManager` while it's
shutting down, like:
```
WARNING [DatabaseManager] Termination request: SystemExit; 0 executing cleanup.
INFO [DatabaseManager] ⏳ Disconnecting Database...
INFO [PID-1|THREAD-29|DatabaseManager|Prisma-82fb1994-4b87-40c1-8869-fbd97bd33fc8] Releasing connection started...
INFO [PID-1|THREAD-29|DatabaseManager|Prisma-82fb1994-4b87-40c1-8869-fbd97bd33fc8] Releasing connection completed successfully.
INFO [DatabaseManager] Terminated.
ERROR POST /create_or_add_to_user_notification_batch failed: Failed to create or add to notification batch for user {user_id} and type AGENT_RUN: NoneType: None
```
This indicates two issues:
- The service doesn't wait for pending RPC calls to finish before
terminating
- We're using `logger.exception` outside an error handling context,
causing the confusing and not much useful `NoneType: None` to be printed
instead of error info
### Changes 🏗️
- Implement graceful shutdown in `AppService` so in-flight RPC calls can
finish
- Add tests for graceful shutdown
- Prevent `AppService` accepting new requests during shutdown
- Rework `AppService` lifecycle management; add support for async
`lifespan`
- Fix `AppService` endpoint error logging
- Improve logging in `AppProcess` and `AppService`
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- Deploy to Dev cluster, then `kubectl rollout restart` the different
services a few times
- [x] -> `DatabaseManager` doesn't break on re-deployment
- [x] -> `Scheduler` doesn't break on re-deployment
- [x] -> `NotificationManager` doesn't break on re-deployment
Updated block costs in `backend/backend/data/block_cost_config.py`:
- **AIShortformVideoCreatorBlock**: Updated from 50 credits to 307
- **AIAdMakerVideoCreatorBlock**: Added cost of 714 credits
- **AIScreenshotToVideoAdBlock**: Added cost of 612 credits
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Verify AIShortformVideoCreatorBlock costs 307 credits when
executed
- [x] Verify AIAdMakerVideoCreatorBlock costs 714 credits when executed
- [x] Verify AIScreenshotToVideoAdBlock costs 612 credits when executed
Updated block costs in `backend/backend/data/block_cost_config.py`:
- **AIShortformVideoCreatorBlock**: Updated from 50 credits to 307
- **AIAdMakerVideoCreatorBlock**: Added cost of 714 credits
- **AIScreenshotToVideoAdBlock**: Added cost of 612 credits
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Verify AIShortformVideoCreatorBlock costs 307 credits when
executed
- [x] Verify AIAdMakerVideoCreatorBlock costs 714 credits when executed
- [x] Verify AIScreenshotToVideoAdBlock costs 612 credits when executed
<!-- Clearly explain the need for these changes: -->
This PR converts Jinja2 TemplateError exceptions to ValueError in the
TextFormatter class to ensure proper error handling and HTTP status code
responses (400 instead of 500).
### Changes 🏗️
<!-- Concisely describe all of the changes made in this pull request:
-->
- Added import for `jinja2.exceptions.TemplateError` in
`backend/util/text.py:6`
- Wrapped template rendering in try-catch block in `format_string`
method (`backend/util/text.py:105-109`)
- Convert `TemplateError` to `ValueError` to ensure proper 400 HTTP
status code for client errors
- Added warning logging for template rendering errors before re-raising
as ValueError
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan: -->
- [x] Verified that invalid Jinja2 templates now raise ValueError
instead of TemplateError
- [x] Confirmed that valid templates continue to work correctly
- [x] Checked that warning logs are generated for template errors
- [x] Validated that the exception chain is preserved with `from e`
#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)
- Run ruff, isort, and black on Python files
- Run prettier on TypeScript files
- Remove unused LaunchDarklyIntegration import from metrics.py
Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>
- Created backend/data/diagnostics.py following Option B data layer pattern
- Refactored diagnostics_admin_routes.py to use the new data layer
- Added endpoints for listing running executions with details
- Added endpoints for stopping executions (single and bulk)
- Created ExecutionsTable component with multi-select and stop buttons
- Integrated execution management table into diagnostics page
Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>
- Removed get_total_agents_count() function
- Removed get_active_agents_count() function
- Updated AgentDiagnosticsResponse model to only include agents_with_active_executions
- Updated frontend to display only agents with active executions metric
Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>
Add new admin diagnostics page to improve on-call diagnostics with the following features:
Backend changes:
- Add ExecutionDiagnosticsResponse and AgentDiagnosticsResponse models
- Create diagnostics_admin_routes.py with endpoints for:
- /admin/diagnostics/executions - Get running, queued (DB), and queued (RabbitMQ) execution counts
- /admin/diagnostics/agents - Get total agents, active agents, and agents with active executions
- Register new diagnostics routes in rest_api.py
- Use Prisma for database queries and direct RabbitMQ connection for queue depth
Frontend changes:
- Add new /admin/diagnostics page with real-time metrics display
- Create DiagnosticsContent component with auto-refresh capability
- Add diagnostic metrics cards for:
- Running executions
- Queued executions (database)
- Queued executions (RabbitMQ)
- Total agents
- Active agents
- Agents with active executions
- Add "System Diagnostics" link to admin navigation sidebar
- Update TypeScript types for new API responses
This improves on-call diagnostics by providing visibility into:
- System load (running executions)
- Queue backlog (DB vs RabbitMQ comparison)
- Agent activity levels
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Co-Authored-By: Claude <noreply@anthropic.com>
- Resolves#11226
### Changes 🏗️
- Drop use of `CloudLoggingHandler` which docs state isn't for use in
GKE
- For cloud logging, output only structured log entries to `stdout`
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Test deploy to dev and check logs
Changes to providers blocks to store in db
### Changes 🏗️
- revet change
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] I have reverted the merge
## Summary
- Fixes database connection warnings in executor logs: "Client is not
connected to the query engine, you must call `connect()` before
attempting to query data"
- Implements resilient database client pattern already used elsewhere in
the codebase
- Adds caching to reduce database load for user context lookups
## Changes
- Updated `get_user_context()` to check `prisma.is_connected()` and fall
back to database manager client
- Added `@cached(maxsize=1000, ttl_seconds=3600)` decorator for
performance optimization
- Updated database manager to expose `get_user_by_id` method
## Test plan
- [x] Verify executor pods no longer show Prisma connection warnings
- [x] Confirm user timezone is still correctly retrieved
- [x] Test fallback behavior when Prisma is disconnected
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Co-authored-by: Claude <noreply@anthropic.com>
We currently try to re-init the LaunchDarkly client every time a feature flag is checked.
This causes 5 second extra latency on the flag check when LD is down, such as now.
Since flag checks are performed on every block execution, this currently cripples the platform's executors.
- Follow-up to #11221
### Changes 🏗️
- Only try to init LaunchDarkly once
- Improve surrounding log statements in the `feature_flag` module
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- This is a critical hotfix; we'll see its effect once deployed
LaunchDarkly is currently down and it's keeping our executor pods from
spinning up.
### Changes 🏗️
- Wrap `LaunchDarklyIntegration` init in a try/except
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- We'll see if it works once it deploys
## Problem
The YouTube transcription block would fail when attempting to transcribe
videos that only had transcripts available in non-English languages.
Even when usable transcripts existed in other languages, the block would
raise a `NoTranscriptFound` error because it only requested English
transcripts.
**Example video that would fail:**
https://www.youtube.com/watch?v=3AMl5d2NKpQ (only has Hungarian
transcripts)
**Error message:**
```
Could not retrieve a transcript for the video https://www.youtube.com/watch?v=3AMl5d2NKpQ!
No transcripts were found for any of the requested language codes: ('en',)
For this video (3AMl5d2NKpQ) transcripts are available in the following languages:
(GENERATED) - hu ("Hungarian (auto-generated)")
```
## Solution
Implemented intelligent language fallback in the
`TranscribeYoutubeVideoBlock.get_transcript()` method:
1. **First**, tries to fetch English transcript (maintains backward
compatibility)
2. **If English unavailable**, lists all available transcripts and
selects the first one using this priority:
- Manually created transcripts (any language)
- Auto-generated transcripts (any language)
3. **Only fails** if no transcripts exist at all
**Example behavior:**
```python
# Before: Video with only Hungarian transcript
get_transcript("3AMl5d2NKpQ") # ❌ Raises NoTranscriptFound
# After: Video with only Hungarian transcript
get_transcript("3AMl5d2NKpQ") # ✅ Returns Hungarian transcript
```
## Changes
- **Modified** `backend/blocks/youtube.py`: Added try-catch logic to
fallback to any available language when English is not found
- **Added** `test/blocks/test_youtube.py`: Comprehensive test suite
covering URL extraction, language fallback, transcript preferences, and
error handling (7 tests)
- **Updated** `docs/content/platform/blocks/youtube.md`: Documented the
language fallback behavior and transcript priority order
## Testing
- ✅ All 7 new unit tests pass
- ✅ Block integration test passes
- ✅ Full test suite: 621 passed, 0 failed (no regressions)
- ✅ Code formatting and linting pass
## Impact
This fix enables the YouTube transcription block to work with
international content while maintaining full backward compatibility:
- ✅ Videos in any language can now be transcribed
- ✅ English is still preferred when available
- ✅ No breaking changes to existing functionality
- ✅ Graceful degradation to available languages
Fixes#10637
Fixes https://linear.app/autogpt/issue/OPEN-2626
> [!WARNING]
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> <details>
> <summary>Firewall rules blocked me from connecting to one or more
addresses (expand for details)</summary>
>
> #### I tried to connect to the following addresses, but was blocked by
firewall rules:
>
> - `www.youtube.com`
> - Triggering command:
`/home/REDACTED/.cache/pypoetry/virtualenvs/autogpt-platform-backend-Ajv4iu2i-py3.11/bin/python3`
(dns block)
>
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these locations, you can either:
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steps](https://gh.io/copilot/actions-setup-steps) to set up my
environment, which run before the firewall is enabled
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<!-- START COPILOT CODING AGENT SUFFIX -->
<details>
<summary>Original prompt</summary>
> Issue Title: if theres only one lanague available for transcribe
youtube return that langage not an error
> Issue Description: `Could not retrieve a transcript for the video
https://www.youtube.com/watch?v=3AMl5d2NKpQ! This is most likely caused
by: No transcripts were found for any of the requested language codes:
('en',) For this video (3AMl5d2NKpQ) transcripts are available in the
following languages: (MANUALLY CREATED) None (GENERATED) - hu
("Hungarian (auto-generated)") (TRANSLATION LANGUAGES) None If you are
sure that the described cause is not responsible for this error and that
a transcript should be retrievable, please create an issue at
https://github.com/jdepoix/youtube-transcript-api/issues. Please add
which version of youtube_transcript_api you are using and provide the
information needed to replicate the error. Also make sure that there are
no open issues which already describe your problem!` you can use this
video to test:
[https://www.youtube.com/watch?v=3AMl5d2NKpQ\`](https://www.youtube.com/watch?v=3AMl5d2NKpQ%60)
> Fixes
https://linear.app/autogpt/issue/OPEN-2626/if-theres-only-one-lanague-available-for-transcribe-youtube-return
>
>
> Comment by User :
> This thread is for an agent session with githubcopilotcodingagent.
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> Comment by User :
> This comment thread is synced to a corresponding [GitHub
issue](https://github.com/Significant-Gravitas/AutoGPT/issues/10637).
All replies are displayed in both locations.
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<!-- Clearly explain the need for these changes: -->
### Need 💡
This PR addresses Linear issue SECRT-1665, which mandates an update to
Linear's OAuth2 implementation. Linear is transitioning from long-lived
access tokens to short-lived access tokens with refresh tokens, with a
deadline of April 1, 2026. This change is crucial to ensure continued
integration with Linear and to support their new token management
system, including a migration path for existing long-lived tokens.
### Changes 🏗️
- **`autogpt_platform/backend/backend/blocks/linear/_oauth.py`**:
- Implemented full support for refresh tokens, including HTTP Basic
Authentication for token refresh requests.
- Added `migrate_old_token()` method to exchange old long-lived access
tokens for new short-lived tokens with refresh tokens using Linear's
`/oauth/migrate_old_token` endpoint.
- Enhanced `get_access_token()` to automatically detect and attempt
migration for old tokens, and to refresh short-lived tokens when they
expire.
- Improved error handling and token expiration management.
- Updated `_request_tokens` to handle both authorization code and
refresh token flows, supporting Linear's recommended authentication
methods.
- **`autogpt_platform/backend/backend/blocks/linear/_config.py`**:
- Updated `TEST_CREDENTIALS_OAUTH` mock data to include realistic
`access_token_expires_at` and `refresh_token` for testing the new token
lifecycle.
- **`LINEAR_OAUTH_IMPLEMENTATION.md`**:
- Added documentation detailing the new Linear OAuth refresh token
implementation, including technical details, migration strategy, and
testing notes.
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Verified OAuth URL generation and parameter encoding.
- [x] Confirmed HTTP Basic Authentication header creation for refresh
requests.
- [x] Tested token expiration logic with a 5-minute buffer.
- [x] Validated migration detection for old vs. new token types.
- [x] Checked code syntax and import compatibility.
#### For configuration changes:
- [ ] `.env.default` is updated or already compatible with my changes
- [ ] `docker-compose.yml` is updated or already compatible with my
changes
- [ ] I have included a list of my configuration changes in the PR
description (under **Changes**)
---
Linear Issue: [SECRT-1665](https://linear.app/autogpt/issue/SECRT-1665)
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