Fixes GHSA-rc89-6g7g-v5v7 / CVE-2026-22038
The logger.info() calls were explicitly logging API keys via
get_secret_value(), exposing credentials in plaintext logs.
Changes:
- Replace info-level credential logging with debug-level provider logging
- Remove all explicit secret value logging from observe/act/extract blocks
Co-authored-by: Otto <otto@agpt.co>
## Changes 🏗️
Adds Redis-based SSE reconnection support for long-running CoPilot
operations (like Agent Generator), enabling clients to reconnect and
resume receiving updates after disconnection.
### What this does:
- **Stream Registry** - Redis-backed task tracking with message
persistence via Redis Streams
- **SSE Reconnection** - Clients can reconnect to active tasks using
`task_id` and `last_message_id`
- **Duplicate Message Fix** - Filters out in-progress assistant messages
from session response when active stream exists
- **Completion Consumer** - Handles background task completion
notifications via Redis Streams
### Architecture:
```
1. User sends message → Backend creates task in Redis
2. SSE chunks written to Redis Stream for persistence
3. Client receives chunks via SSE subscription
4. If client disconnects → Task continues in background
5. Client reconnects → GET /sessions/{id} returns active_stream info
6. Client subscribes to /tasks/{task_id}/stream with last_message_id
7. Missed messages replayed from Redis Stream
```
### Key endpoints:
- `GET /sessions/{session_id}` - Returns `active_stream` info if task is
running
- `GET /tasks/{task_id}/stream?last_message_id=X` - SSE endpoint for
reconnection
- `GET /tasks/{task_id}` - Get task status
- `POST /operations/{op_id}/complete` - Webhook for external service
completion
### Duplicate message fix:
When `GET /sessions/{id}` detects an active stream:
1. Filters out the in-progress assistant message from response
2. Returns `last_message_id="0-0"` so client replays stream from
beginning
3. Client receives complete response only through SSE (single source of
truth)
### Frontend changes:
- Task persistence in localStorage for cross-tab reconnection
- Stream event dispatcher handles reconnection flow
- Deduplication logic prevents duplicate messages
### Testing:
- Manual testing of reconnection scenarios
- Verified duplicate message fix works correctly
## Related
- Resolves SSE timeout issues for Agent Generator
- Fixes duplicate message bug on reconnection
## Summary
Adds a new copilot tool that allows users to customize
marketplace/template agents using natural language before adding them to
their library.
This exposes the Agent Generator's `/api/template-modification` endpoint
to the copilot, which was previously not available.
## Changes
- **service.py**: Add `customize_template_external` to call Agent
Generator's template modification endpoint
- **core.py**:
- Add `customize_template` wrapper function
- Extract `graph_to_json` as a reusable function (was previously inline
in `get_agent_as_json`)
- **customize_agent.py**: New tool that:
- Takes marketplace agent ID (format: `creator/slug`)
- Fetches template from store via `store_db.get_agent()`
- Calls Agent Generator for customization
- Handles clarifying questions from the generator
- Saves customized agent to user's library
- **__init__.py**: Register the tool in `TOOL_REGISTRY` for
auto-discovery
## Usage Flow
1. User searches marketplace: *"Find me a newsletter agent"*
2. Copilot calls `find_agent` → returns `autogpt/newsletter-writer`
3. User: *"Customize that agent to post to Discord instead of email"*
4. Copilot calls:
```
customize_agent(
agent_id="autogpt/newsletter-writer",
modifications="Post to Discord instead of sending email"
)
```
5. Agent Generator may ask clarifying questions (e.g., "What Discord
channel?")
6. Customized agent is saved to user's library
## Test plan
- [x] Verified tool imports correctly
- [x] Verified tool is registered in `TOOL_REGISTRY`
- [x] Verified OpenAI function schema is valid
- [x] Ran existing tests (`pytest backend/api/features/chat/tools/`) -
all pass
- [x] Type checker (`pyright`) passes with 0 errors
- [ ] Manual testing with copilot (requires Agent Generator service)
## Summary
Adds retry logic and graceful error handling to `initialize_blocks()` to
prevent transient DB errors from crashing server startup.
## Problem
When a transient database error occurs during block initialization
(e.g., Prisma P1017 "Server has closed the connection"), the entire
server fails to start. This is overly aggressive since:
1. Blocks are already registered in memory
2. The DB sync is primarily for tracking/schema storage
3. One flaky connection shouldn't prevent the server from starting
**Triggered by:** [Sentry
AUTOGPT-SERVER-7PW](https://significant-gravitas.sentry.io/issues/7238733543/)
## Solution
- Add retry decorator (3 attempts with exponential backoff) for DB
operations
- On failure after retries, log a warning and continue to the next block
- Blocks remain available in memory even if DB sync fails
- Log summary of any failed blocks at the end
## Changes
- `autogpt_platform/backend/backend/data/block.py`: Wrap block DB sync
in retry logic with graceful fallback
## Testing
- Existing block initialization behavior unchanged on success
- On transient DB errors: retries up to 3 times, then continues with
warning
## Summary
Routes `increment_onboarding_runs` and `cleanup_expired_oauth_tokens`
through the DatabaseManager RPC client instead of calling Prisma
directly.
## Problem
The Scheduler service never connects its Prisma client. While
`add_graph_execution()` in `utils.py` has a fallback that routes through
DatabaseManager when Prisma isn't connected, subsequent calls in the
scheduler were hitting Prisma directly:
- `increment_onboarding_runs()` after successful graph execution
- `cleanup_expired_oauth_tokens()` in the scheduled job
These threw `ClientNotConnectedError`, caught by generic exception
handlers but spamming Sentry (~696K events since December per the
original analysis in #11926).
## Solution
Follow the same pattern as `utils.py`:
1. Add `cleanup_expired_oauth_tokens` to `DatabaseManager` and
`DatabaseManagerAsyncClient`
2. Update scheduler to use `get_database_manager_async_client()` for
both calls
## Changes
- **database.py**: Import and expose `cleanup_expired_oauth_tokens` in
both manager classes
- **scheduler.py**: Use `db.increment_onboarding_runs()` and
`db.cleanup_expired_oauth_tokens()` via the async client
## Impact
- Eliminates Sentry error spam from scheduler
- Onboarding run counters now actually increment for scheduled
executions
- OAuth token cleanup now actually runs
## Testing
Deploy to staging with scheduled graphs and verify:
1. No more `ClientNotConnectedError` in scheduler logs
2. `UserOnboarding.agentRuns` increments on scheduled runs
3. Expired OAuth tokens get cleaned up
Refs: #11926 (original fix that was closed)
Fixing
https://github.com/Significant-Gravitas/AutoGPT/pull/11297#discussion_r2496833421
### Changes 🏗️
1. event_bus.py - Added close method to AsyncRedisEventBus
- Added __init__ method to track the _pubsub instance attribute
- Added async def close() method that closes the PubSub connection
safely
- Modified listen_events() to store the pubsub reference in self._pubsub
2. ws_api.py - Added cleanup in event_broadcaster
- Wrapped the worker coroutines in try/finally block
- The finally block calls close() on both event buses to ensure cleanup
happens on any exit (including exceptions before retry)
- Replaced custom expandable sections with ToolAccordion component in both FindAgents and SearchDocs tools.
- Simplified state management by removing unnecessary useState and useReducedMotion hooks.
- Enhanced accessibility and readability of agent and document search results with clearer descriptions and structured layouts.
## Summary
Fixes CoPilot becoming unresponsive after long-running tools complete,
and refactors context window management into a reusable function.
## Problem
After `create_agent` completes, `_generate_llm_continuation()` was
sending ALL messages to OpenRouter without any context compaction. When
conversations exceeded ~50 messages, OpenRouter rejected requests with
`provider_name: 'unknown'` (no provider would accept).
**Evidence:** Langfuse session
[44fbb803-092e-4ebd-b288-852959f4faf5](https://cloud.langfuse.com/project/cmk5qhf210003ad079sd8utjt/sessions/44fbb803-092e-4ebd-b288-852959f4faf5)
showed:
- Successful calls: 32-50 messages, known providers
- Failed calls: 52+ messages, `provider: unknown`, `completion: null`
## Changes
### Refactor: Extract reusable `_manage_context_window()`
- Counts tokens and checks against 120k threshold
- Summarizes old messages while keeping recent 15
- Ensures tool_call/tool_response pairs stay intact
- Progressive truncation if still over limit
- Returns `ContextWindowResult` dataclass with messages, token count,
compaction status, and errors
- Helper `_messages_to_dicts()` reduces code duplication
### Fix: Update `_generate_llm_continuation()`
- Now calls `_manage_context_window()` before making LLM calls
- Adds retry logic with exponential backoff (matching
`_stream_chat_chunks` behavior)
### Cleanup: Update `_stream_chat_chunks()`
- Replaced inline context management with call to
`_manage_context_window()`
- Eliminates code duplication between the two functions
## Testing
- Syntax check: ✅
- Ruff lint: ✅
- Import verification: ✅
## Checklist
- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my own code
- [x] My changes generate no new warnings
- [x] I have checked that my changes do not break existing functionality
---------
Co-authored-by: Otto <otto@agpt.co>
## Summary
Updates the Agent Generator client to enrich the description with
context before calling, instead of sending `user_instruction` as a
separate parameter.
## Context
Companion PR to Significant-Gravitas/AutoGPT-Agent-Generator#105 which
removes unused parameters from the decompose API.
## Changes
- Enrich `description` with `context` (e.g., clarifying question
answers) before sending
- Remove `user_instruction` from request payload
## How it works
Both input boxes and chat box work the same way - the frontend
constructs a formatted message with answers and sends it as a user
message. The backend then enriches the description with this context
before calling the external Agent Generator service.
## Summary
Fixes a bug where the fallback path in context compaction passes
`recent_messages` (already sliced) instead of `messages_dict` (full
conversation) to `_ensure_tool_pairs_intact`.
This caused the function to fail to find assistant messages that exist
in the original conversation but were outside the sliced window,
resulting in orphan tool_results being sent to Anthropic and rejected
with:
```
messages.66.content.0: unexpected tool_use_id found in tool_result blocks: toolu_vrtx_019bi1PDvEn7o5ByAxcS3VdA
```
## Changes
- Pass `messages_dict` and `slice_start` (relative to full conversation)
instead of `recent_messages` and `reduced_slice_start` (relative to
already-sliced list)
## Testing
This is a targeted fix for the fallback path. The bug only manifests
when:
1. Token count > 120k (triggers compaction)
2. Initial compaction + summary still exceeds limit (triggers fallback)
3. A tool_result's corresponding assistant is in `messages_dict` but not
in `recent_messages`
## Related
- Fixes SECRT-1861
- Related: SECRT-1839 (original fix that missed this code path)
- Updated the `FindBlocksTool` to utilize the new `MorphingTextAnimation` for improved visual feedback.
- Refactored `MorphingTextAnimation` to accept a `text` prop, simplifying its usage and enhancing flexibility.
- Improved the rendering logic in `ChatMessagesContainer` to ensure proper key assignment for dynamic elements.
These changes aim to enhance the user experience by providing better visual transitions and cleaner component interactions.
- Simplified the `handleMessageSubmit` function in the chat page for better readability.
- Refactored the `ChatMessagesContainer` to improve message rendering logic, including the addition of the `FindBlocksTool` for tool call outputs.
- Updated the `ChatSidebar` component for better organization and clarity in props definition.
- Introduced a new `MorphingTextAnimation` component to enhance visual feedback during message transitions.
- Removed the obsolete `chat-store.ts` file to streamline the codebase.
These changes aim to improve the overall functionality and user experience of the chat interface.
### Changes 🏗️
- Add a unit test to verify webhook ingress URL generation matches the
FastAPI route.
### 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] poetry run pytest backend/integrations/webhooks/utils_test.py
--confcutdir=backend/integrations/webhooks
#### 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)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Tests**
* Added a unit test that validates webhook ingress URL generation
matches the application's resolved route (scheme, host, and path) for
provider-specific webhook endpoints, improving confidence in routing
behavior and helping prevent regressions.
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Reinier van der Leer <pwuts@agpt.co>
## Problem
When marketplace agents are included in the `library_agents` payload
sent to the Agent Generator service, they were missing required fields
(`graph_id`, `graph_version`, `input_schema`, `output_schema`). This
caused Pydantic validation to fail with HTTP 422 Unprocessable Entity.
**Root cause:** The `MarketplaceAgentSummary` TypedDict had a different
shape than `LibraryAgentInfo` expected by the Agent Generator:
- Agent Generator expects: `graph_id`, `graph_version`, `name`,
`description`, `input_schema`, `output_schema`
- MarketplaceAgentSummary had: `name`, `description`, `sub_heading`,
`creator`, `is_marketplace_agent`
## Solution
1. **Add `agent_graph_id` to `StoreAgent` model** - The field was
already in the database view but not exposed
2. **Include `agentGraphId` in hybrid search SQL query** - Carry the
field through the search CTEs
3. **Update `search_marketplace_agents_for_generation()`** - Now fetches
full graph schemas using `get_graph()` and returns `LibraryAgentSummary`
(same type as library agents)
4. **Update deduplication logic** - Use `graph_id` instead of name for
more accurate deduplication
## Changes
- `backend/api/features/store/model.py`: Add optional `agent_graph_id`
field to `StoreAgent`
- `backend/api/features/store/hybrid_search.py`: Include `agentGraphId`
in SQL query columns
- `backend/api/features/store/db.py`: Map `agentGraphId` when creating
`StoreAgent` objects
- `backend/api/features/chat/tools/agent_generator/core.py`: Update
`search_marketplace_agents_for_generation()` to fetch and include full
graph schemas
## Testing
- [ ] Agent creation on dev with marketplace agents in context
- [ ] Verify no 422 errors from Agent Generator
- [ ] Verify marketplace agents can be used as sub-agents
Fixes: SECRT-1817
---------
Co-authored-by: majdyz <majdyz@users.noreply.github.com>
Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
## Summary
Addresses #11785 - users were encountering `openai_api_key_credentials`
errors when following the create-basic-agent guide because it didn't
mention the need to configure API credentials before using AI blocks.
## Changes
Added a **Prerequisites** section to
`docs/platform/create-basic-agent.md` explaining:
- **Cloud users:** Go to Profile → Integrations to add API keys
- **Self-hosted (Docker):** Add keys to `autogpt_platform/backend/.env`
and restart services
Also added a note that the Calculator example doesn't need credentials,
making it a good first test.
## Related
- Issue: #11785
## Context
When users ask the chat to create agents, they may want to compose
workflows that reuse their existing agents as sub-agents. For this to
work, the Agent Generator service needs to know what agents the user has
available.
**Challenge:** Users can have large libraries with many agents. Fetching
all of them would be slow and provide too much context to the LLM.
## Solution
This PR implements **search-based library agent fetching** with a
**two-phase search** strategy:
1. **Phase 1 (Initial Search):** When the user describes their goal, we
search for relevant library agents using the goal as the search query
2. **Phase 2 (Step-Based Enrichment):** After the goal is decomposed
into steps, we extract keywords from those steps and search for
additional relevant agents
This ensures we find agents that are relevant to both the high-level
goal AND the specific steps identified.
### Example Flow
```
User goal: "Create an agent that fetches weather and sends a summary email"
Phase 1: Search for "weather email summary" → finds "Weather Fetcher" agent
Phase 2: After decomposition identifies steps like "send email notification"
→ searches "send email notification" → finds "Gmail Sender" agent
```
### Changes
**Library Agent Fetching:**
- `get_library_agents_for_generation()` - Search-based fetching from
user's library
- `search_marketplace_agents_for_generation()` - Search public
marketplace
- `get_all_relevant_agents_for_generation()` - Combines both with
deduplication
**Two-Phase Search:**
- `extract_search_terms_from_steps()` - Extracts keywords from
decomposed steps
- `enrich_library_agents_from_steps()` - Searches for additional agents
based on steps
- Integrated into `create_agent.py` as "Step 1.5" after goal
decomposition
**Type Safety:**
- Added `TypedDict` definitions: `LibraryAgentSummary`,
`MarketplaceAgentSummary`, `DecompositionStep`, `DecompositionResult`
### Design Decisions
- **Search-based, not fetch-all:** Scalable for large libraries
- **Library agents prioritized:** They have full schemas; marketplace
agents have basic info only
- **Deduplication by name and graph_id:** Prevents duplicates across
searches
- **Graceful degradation:** Failures don't block agent generation
- **Limited to 3 search terms:** Avoids excessive API calls during
enrichment
## Related PR
- Agent Generator:
https://github.com/Significant-Gravitas/AutoGPT-Agent-Generator/pull/103
## Test plan
- [x] `test_library_agents.py` - 19 tests covering all new functions
- [x] `test_service.py` - 4 tests for library_agents passthrough
- [ ] Integration test: Create agent with library sub-agent composition
### Changes 🏗️
Further improvements to LaunchDarkly initialisation and homepage
redirect...
### 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 the app locally with the flag disabled/enabled, and the
redirects work
---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Ubbe <0ubbe@users.noreply.github.com>
## Changes 🏗️
Removes the `key` prop from `LDProvider` that was causing full remounts
when user context changed.
### The Problem
The `key={context.key}` prop was forcing React to unmount and remount
the entire LDProvider when switching from anonymous → logged in user:
```
1. Page loads, user loading → key="anonymous" → LD mounts → flags available ✅
2. User finishes loading → key="user-123" → React sees key changed
3. LDProvider UNMOUNTS → flags become undefined ❌
4. New LDProvider MOUNTS → initializes again → flags available ✅
```
This caused the flag values to cycle: `undefined → value → undefined →
value`
### The Fix
Remove the `key` prop. The LDProvider handles context changes internally
via the `context` prop, which triggers `identify()` without remounting
the provider.
## Checklist 📋
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [ ] I have tested my changes according to the test plan:
- [ ] Flag values don't flicker on page load
- [ ] Flag values update correctly when logging in/out
- [ ] No redirect race conditions
Related: SECRT-1845
[SECRT-1842: run_agent tool does not correctly use credentials - agents
fail with insufficient auth
scopes](https://linear.app/autogpt/issue/SECRT-1842)
### Changes 🏗️
- Include scopes in credentials filter in
`backend.api.features.chat.tools.utils.match_user_credentials_to_graph`
### 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:
- CI must pass
- It's broken now and a simple change so we'll test in the dev
deployment
- Added new dependencies for Streamdown components to improve rendering capabilities.
- Updated the chat page layout to utilize new conversation components, enhancing user experience.
- Refactored message handling to streamline input submission and improve message rendering logic.
These changes aim to enhance the overall functionality and usability of the chat interface.
### Changes 🏗️
The page used a client-side redirect (`useEffect` + `router.replace`)
which only works after JavaScript loads and hydrates. On deployed sites,
if there's any delay or failure in JS execution, users see an
empty/black page because the component returns null.
**Fix:** Converted to a server-side redirect using redirect() from
next/navigation. This is a server component now, so:
### 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 locally but will see it fully working once deployed
- Refactored the chat page to utilize a new `ChatSidebar` component for better organization and user experience.
- Improved message handling by simplifying session creation logic and ensuring proper state management.
- Updated UI elements for consistency, including button labels and input handling.
- Enhanced message rendering to support tool call outputs, improving the chat interaction flow.
These changes aim to streamline the chat interface and improve overall usability.
- Added `_continuation_message_id` parameter to `stream_chat_completion` to allow reuse of message IDs for tool call follow-ups.
- Modified message yielding logic to prevent duplicate messages when reusing IDs.
- Ensured that the message start is only yielded for the initial call, improving message handling during continuations.
This change enhances the chat completion flow by maintaining message integrity and reducing redundancy in message handling.
## Summary
Remove `claude-3-7-sonnet-20250219` from LLM model definitions ahead of
Anthropic's API retirement, with comprehensive migration and defensive
error handling.
## Background
Anthropic is retiring Claude 3.7 Sonnet (`claude-3-7-sonnet-20250219`)
on **February 19, 2026 at 9:00 AM PT**. This PR removes the model from
the platform and migrates existing users to prevent service
interruptions.
## Changes
### Code Changes
- Remove `CLAUDE_3_7_SONNET` enum member from `LlmModel` in `llm.py`
- Remove corresponding `ModelMetadata` entry
- Remove `CLAUDE_3_7_SONNET` from `StagehandRecommendedLlmModel` enum
- Remove `CLAUDE_3_7_SONNET` from block cost config
- Add `CLAUDE_4_5_SONNET` to `StagehandRecommendedLlmModel` enum
- Update Stagehand block defaults from `CLAUDE_3_7_SONNET` to
`CLAUDE_4_5_SONNET` (staying in Claude family)
- Add defensive error handling in `CredentialsFieldInfo.discriminate()`
for deprecated model values
### Database Migration
- Adds migration `20260126120000_migrate_claude_3_7_to_4_5_sonnet`
- Migrates `AgentNode.constantInput` model references
- Migrates `AgentNodeExecutionInputOutput.data` preset overrides
### Documentation
- Updated `docs/integrations/block-integrations/llm.md` to remove
deprecated model
- Updated `docs/integrations/block-integrations/stagehand/blocks.md` to
remove deprecated model and add Claude 4.5 Sonnet
## Notes
- Agent JSON files in `autogpt_platform/backend/agents/` still reference
this model in their provider mappings. These are auto-generated and
should be regenerated separately.
## Testing
- [ ] Verify LLM block still functions with remaining models
- [ ] Confirm no import errors in affected files
- [ ] Verify migration runs successfully
- [ ] Verify deprecated model gives helpful error message instead of
KeyError
## Changes 🏗️
Fixes the hard refresh redirect bug (SECRT-1845) by removing the double
feature flag check.
### Before (buggy)
```
/ → checks flag → /copilot or /library
/copilot (layout) → checks flag → /library if OFF
```
On hard refresh, two sequential LD checks created a race condition
window.
### After (fixed)
```
/ → always redirects to /copilot
/copilot (layout) → single flag check via FeatureFlagPage
```
Single check point = no double-check race condition.
## Root Cause
As identified by @0ubbe: the root page and copilot layout were both
checking the feature flag. On hard refresh with network latency, the
second check could fire before LaunchDarkly fully initialized, causing
users to be bounced to `/library`.
## Test Plan
- [ ] Hard refresh on `/` → should go to `/copilot` (flag ON)
- [ ] Hard refresh on `/copilot` → should stay on `/copilot` (flag ON)
- [ ] With flag OFF → should redirect to `/library`
- [ ] Normal navigation still works
Fixes: SECRT-1845
cc @0ubbe
## Summary
Refocuses the chat input textarea after voice transcription finishes,
allowing users to immediately use `spacebar+enter` to record and send
their prompt.
## Changes
- Added `inputId` parameter to `useVoiceRecording` hook
- After transcription completes, the input is automatically focused
- This improves the voice input UX flow
## Testing
1. Click mic button or press spacebar to record voice
2. Record a message and stop
3. After transcription completes, the input should be focused
4. User can now press Enter to send or spacebar to record again
---------
Co-authored-by: Lluis Agusti <hi@llu.lu>
## Summary
Fixes flaky E2E marketplace and library tests that were causing PRs to
be removed from the merge queue.
## Root Cause
1. **Test data was probabilistic** - `e2e_test_data.py` used random
chances (40% approve, then 20-50% feature), which could result in 0
featured agents
2. **Library pagination threshold wrong** - Checked `>= 10`, but page
size is 20
3. **Fixed timeouts** - Used `waitForTimeout(2000)` /
`waitForTimeout(10000)` instead of proper waits
## Changes
### Backend (`e2e_test_data.py`)
- Add guaranteed minimums: 8 featured agents, 5 featured creators, 10
top agents
- First N submissions are deterministically approved and featured
- Increase agents per user from 15 → 25 (for pagination with
page_size=20)
- Fix library agent creation to use constants instead of hardcoded `10`
### Frontend Tests
- `library.spec.ts`: Fix pagination threshold to `PAGE_SIZE` (20)
- `library.page.ts`: Replace 2s timeout with `networkidle` +
`waitForFunction`
- `marketplace.page.ts`: Add `networkidle` wait, 30s waits in
`getFirst*` methods
- `marketplace.spec.ts`: Replace 10s timeout with `waitForFunction`
- `marketplace-creator.spec.ts`: Add `networkidle` + element waits
## Related
- Closes SECRT-1848, SECRT-1849
- Should unblock #11841 and other PRs in merge queue
---------
Co-authored-by: Ubbe <hi@ubbe.dev>
### Changes 🏗️
- Removed `cd docs && pip install -r requirements.txt` from
`postCreateCmd` in `.branchlet.json`
- Docs dependencies will no longer be auto-installed during branchlet
worktree creation
### Rationale
The docs setup step was adding unnecessary overhead to the worktree
creation process. Developers who need to work on documentation can
manually install the docs requirements 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:
- [x] Verified branchlet worktree creation still works without the docs
pip install step
#### 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**)
