Filter out library agents with ERROR status when searching for
sub-agent composition candidates. This prevents recommending broken
or draft agents that have failed executions.
Add configurable include_library parameter (default True) to allow
controlling whether user's library agents are included in the search
results for sub-agent composition.
[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
## 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
## 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>
The "View in Library" link was returning 404 because the path was
missing the /agents/ segment. Fixed in both create_agent.py and
edit_agent.py to match the correct route used elsewhere.
- Add null checks for .lower() on agent names that could be None
- Add isinstance guard for non-string step values in extract_search_terms
- Re-raise DatabaseError instead of swallowing it in agent_search
- Remove inline comments per style guidelines
- Add LLM continuation call when background tool execution fails with
exception (previously users saw no explanation for errors)
- Improve validation error messages with more helpful guidance
- Add error_details parameter to include technical context in error
responses when needed
- Update create_agent to pass error details for validation failures
## 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>
Previously, searching for "flight price drop alert" required that exact
phrase to be in the agent name/description. Now it splits into individual
words and matches agents containing ANY of: flight, price, drop, alert.
This fixes the issue where "flight price tracker" wasn't found when
searching for "flight price drop alert" even though they share keywords.
Added logging to help diagnose library search issues:
- Log the query and user_id when tool is called
- Log the number of results returned from database
When users paste a library URL or agent UUID, the find_library_agent
tool now does direct ID lookup first (both by graph_id and library
agent ID) before falling back to text search.
This fixes the issue where searching by UUID would fail because
it was only doing text matching on agent names/descriptions.
When users paste library URLs (e.g., /library/agents/{id}), the ID is
the LibraryAgent primary key, not the graph_id. The previous code only
looked up by graph_id, causing "agent not found" errors.
Now get_library_agent_by_id() tries both lookup strategies:
1. First by graph_id (AgentGraph primary key)
2. Then by library agent ID (LibraryAgent primary key)
This fixes the issue where users couldn't reference agents by pasting
their library URLs in chat.
When users mention agents by UUID in their goal description, we now:
1. Extract UUID v4 patterns from the search_query text
2. Fetch those agents directly by graph_id
3. Include them in the library_agents list for the LLM
This ensures explicitly referenced agents are always available to the
Agent Generator, even if text search wouldn't find them.
Added:
- extract_uuids_from_text(): extracts UUID v4 patterns from text
- get_library_agent_by_graph_id(): fetches a single agent by graph_id
- Integration in get_all_relevant_agents_for_generation()
- Move store_db, get_graph, get_graph_all_versions imports to top-level
- Catch specific NotFoundError instead of generic Exception
- Cleaner code organization following standard Python conventions
get_agent_as_json claimed to accept both graph IDs and library agent IDs
but only tried direct graph lookup. When a library agent ID was passed,
the function would return None (agent_not_found error).
Now the function:
1. First tries direct graph lookup with the provided ID
2. If not found, resolves the ID as a library agent ID to get the graph_id
3. Then fetches the graph using the resolved graph_id
- Add TypedDict types for agent summaries (LibraryAgentSummary, MarketplaceAgentSummary, DecompositionResult)
- Add extract_search_terms_from_steps() to extract keywords from decomposed instructions
- Add enrich_library_agents_from_steps() for two-phase search after decomposition
- Integrate enrichment into create_agent.py flow
- Add comprehensive tests for new functionality
### 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**)
- Add try/except error handling to get_library_agents_for_generation
for graceful degradation (consistent with marketplace search)
- Add null checks when deduplicating agents by name to prevent
AttributeError if agent name is None
- Use actual graph ID from current_agent in edit_agent.py to properly
exclude the agent being edited (agent_id might be a library agent ID)
- Add get_library_agents_for_generation() with search_term support
- Add search_marketplace_agents_for_generation() for marketplace search
- Add get_all_relevant_agents_for_generation() combining both sources
- Update service.py to pass library_agents in all requests
- Update create_agent.py to fetch and pass relevant library agents
- Update edit_agent.py to fetch and pass relevant library agents
- Add tests for library agent fetching and passthrough
## Summary
- Add helper functions in `service.py` to create standardized error
responses with `error_type` classification
- Update service functions to return error dicts instead of `None`,
preserving error details from the Agent Generator microservice
- Update `core.py` to pass through error responses properly
- Update `create_agent.py` to handle error responses with user-friendly
messages based on error type
## Error Types Now Propagated
| Error Type | Description | User Message |
|------------|-------------|--------------|
| `llm_parse_error` | LLM returned unparseable response | "The AI had
trouble understanding this request" |
| `llm_timeout` / `timeout` | Request timed out | "The request took too
long" |
| `llm_rate_limit` / `rate_limit` | Rate limited | "The service is
currently busy" |
| `validation_error` | Agent validation failed | "The generated agent
failed validation" |
| `connection_error` | Could not connect to Agent Generator | Generic
error message |
| `http_error` | HTTP error from Agent Generator | Generic error message
|
| `unknown` | Unclassified error | Generic error message |
## Motivation
This enables better debugging for issues like SECRT-1817 where
decomposition failed due to transient LLM errors but the root cause was
unclear in the logs. Now:
1. Error details from the Agent Generator microservice are preserved
2. Users get more helpful error messages based on error type
3. Debugging is easier with `error_type` in response details
## Related PR
- Agent Generator side:
https://github.com/Significant-Gravitas/AutoGPT-Agent-Generator/pull/102
## Test Plan
- [ ] Test decomposition with various error scenarios (timeout, parse
error)
- [ ] Verify user-friendly messages are shown based on error type
- [ ] Check that error details are logged properly
Split `autogpt_platform/CLAUDE.md` into project-specific files, to make
the scope of the instructions clearer.
Also, some minor improvements:
- Change references to other Markdown files to @file/path.md syntax that
Claude recognizes
- Update ambiguous/incorrect/outdated instructions
- Remove trailing slashes
- Fix broken file path references in other docs (including comments)
## Changes 🏗️https://github.com/user-attachments/assets/d9c12ac0-625c-4b38-8834-e494b5eda9c0
Add a "speech to text" feature in the Chat input fox of Copilot, similar
as what you have in ChatGPT.
## 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 locally and try the speech to text feature as part of the chat
input box
### For configuration changes:
We need to add `OPENAI_API_KEY=` to Vercel ( used in the Front-end )
both in Dev and Prod.
- [x] `.env.default` is updated or already compatible with my changes
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Implements persistent User Workspace storage for CoPilot, enabling
blocks to save and retrieve files across sessions. Files are stored in
session-scoped virtual paths (`/sessions/{session_id}/`).
Fixes SECRT-1833
### Changes 🏗️
**Database & Storage:**
- Add `UserWorkspace` and `UserWorkspaceFile` Prisma models
- Implement `WorkspaceStorageBackend` abstraction (GCS for cloud, local
filesystem for self-hosted)
- Add `workspace_id` and `session_id` fields to `ExecutionContext`
**Backend API:**
- Add REST endpoints: `GET/POST /api/workspace/files`, `GET/DELETE
/api/workspace/files/{id}`, `GET /api/workspace/files/{id}/download`
- Add CoPilot tools: `list_workspace_files`, `read_workspace_file`,
`write_workspace_file`
- Integrate workspace storage into `store_media_file()` - returns
`workspace://file-id` references
**Block Updates:**
- Refactor all file-handling blocks to use unified `ExecutionContext`
parameter
- Update media-generating blocks to persist outputs to workspace
(AIImageGenerator, AIImageCustomizer, FluxKontext, TalkingHead, FAL
video, Bannerbear, etc.)
**Frontend:**
- Render `workspace://` image references in chat via proxy endpoint
- Add "AI cannot see this image" overlay indicator
**CoPilot Context Mapping:**
- Session = Agent (graph_id) = Run (graph_exec_id)
- Files scoped to `/sessions/{session_id}/`
### Checklist 📋
#### For code changes:
- [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:
- [ ] Create CoPilot session, generate image with AIImageGeneratorBlock
- [ ] Verify image returns `workspace://file-id` (not base64)
- [ ] Verify image renders in chat with visibility indicator
- [ ] Verify workspace files persist across sessions
- [ ] Test list/read/write workspace files via CoPilot tools
- [ ] Test local storage backend for self-hosted deployments
#### 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**)
🤖 Generated with [Claude Code](https://claude.ai/code)
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Introduces a new persistent file-storage surface area (DB tables,
storage backends, download API, and chat tools) and rewires
`store_media_file()`/block execution context across many blocks, so
regressions could impact file handling, access control, or storage
costs.
>
> **Overview**
> Adds a **persistent per-user Workspace** (new
`UserWorkspace`/`UserWorkspaceFile` models plus `WorkspaceManager` +
`WorkspaceStorageBackend` with GCS/local implementations) and wires it
into the API via a new `/api/workspace/files/{file_id}/download` route
(including header-sanitized `Content-Disposition`) and shutdown
lifecycle hooks.
>
> Extends `ExecutionContext` to carry execution identity +
`workspace_id`/`session_id`, updates executor tooling to clone
node-specific contexts, and updates `run_block` (CoPilot) to create a
session-scoped workspace and synthetic graph/run/node IDs.
>
> Refactors `store_media_file()` to require `execution_context` +
`return_format` and to support `workspace://` references; migrates many
media/file-handling blocks and related tests to the new API and to
persist generated media as `workspace://...` (or fall back to data URIs
outside CoPilot), and adds CoPilot chat tools for
listing/reading/writing/deleting workspace files with safeguards against
context bloat.
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
6abc70f793. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Reinier van der Leer <pwuts@agpt.co>
## Summary
- Add `InvalidInputError` for validation errors (search term too long,
invalid pagination) - returns 400 instead of 500
- Remove redundant try/catch blocks in library routes - global exception
handlers already handle `ValueError`→400 and `NotFoundError`→404
- Aggregate embedding backfill errors and log once at the end instead of
per content type to prevent Sentry issue spam
## Test plan
- [x] Verify validation errors (search term >100 chars) return 400 Bad
Request
- [x] Verify NotFoundError still returns 404
- [x] Verify embedding errors are logged once at the end with aggregated
counts
Fixes AUTOGPT-SERVER-7K5, BUILDER-6NC
---------
Co-authored-by: Swifty <craigswift13@gmail.com>
Disable automatic onboarding redirects on signup/login while keeping the
checklist/wallet functional. Users now receive $5 (500 credits) on their
first visit to /copilot.
### Changes 🏗️
- **Frontend**: `shouldShowOnboarding()` now returns `false`, disabling
auto-redirects to `/onboarding`
- **Backend**: Added `VISIT_COPILOT` onboarding step with 500 credit
($5) reward
- **Frontend**: Copilot page automatically completes `VISIT_COPILOT`
step on mount
- **Database**: Migration to add `VISIT_COPILOT` to `OnboardingStep`
enum
NOTE: /onboarding/1-welcome -> /library now as shouldShowOnboardin is
always false
Users land directly on `/copilot` after signup/login and receive $5
invisibly (not shown in checklist UI).
### 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] New user signup (email/password) → lands on `/copilot`, wallet
shows 500 credits
- [x] Verified credits are only granted once (idempotent via onboarding
reward mechanism)
- [x] Existing user login (already granted flag set) → lands on
`/copilot`, no duplicate credits
- [x] Checklist/wallet remains functional
#### 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**)
No configuration changes required.
---
OPEN-2967
🤖 Generated with [Claude Code](https://claude.ai/code)
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Introduces a new onboarding step and adjusts onboarding flow.
>
> - Adds `VISIT_COPILOT` onboarding step (+500 credits) with DB enum
migration and API/type updates
> - Copilot page auto-completes `VISIT_COPILOT` on mount to grant the
welcome bonus
> - Changes `/onboarding/enabled` to require user context and return
`false` when `CHAT` feature is enabled (skips legacy onboarding)
> - Wallet now refreshes credits on any onboarding `step_completed`
notification; confetti limited to visible tasks
> - Test flows updated to accept redirects to `copilot`/`library` and
verify authenticated state
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
ec5a5a4dfd. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>
## Summary
Fixes the issue where the "Creating Agent" spinner doesn't auto-update
when agent generation completes - user had to refresh the browser.
**Changes:**
- **Frontend polling**: Add `onOperationStarted` callback to trigger
polling when `operation_started` is received via SSE
- **Polling backoff**: 2s, 4s, 6s, 8s... up to 30s max
- **Message deduplication**: Use content-based keys (role + content)
instead of timestamps to prevent duplicate messages
- **Message ordering**: Preserve server message order instead of
timestamp-based sorting
- **Debug cleanup**: Remove verbose console.log/console.info statements
## Test plan
- [ ] Start agent generation in copilot
- [ ] Verify "Creating Agent" spinner appears
- [ ] Wait for completion (2-5 min) WITHOUT refreshing
- [ ] Verify agent carousel appears automatically when done
- [ ] Verify no duplicate messages in chat
- [ ] Verify message order is correct (user → assistant → tool_call →
tool_response)
<!-- Clearly explain the need for these changes: -->
Config change to increase the max times an agent can run in the chat and
the max number of scheduels created by copilot in one chat
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Increases per-chat operational limits for Copilot.
>
> - Bumps `max_agent_runs` default from `3` to `30` in `ChatConfig`
> - Bumps `max_agent_schedules` default from `3` to `30` in `ChatConfig`
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
93cbae6d27. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
<!-- Clearly explain the need for these changes: -->
oops file
### Changes 🏗️
<!-- Concisely describe all of the changes made in this pull request:
-->
removes file that should have not been commited
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Removes erroneous `backend/blocks/video/__init__.py`, eliminating an
unintended `video` package.
>
> - Deletes a placeholder comment-only file
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
3b84576c33. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
## Summary
Fixes context compaction breaking tool_call/tool_response pairs, causing
API validation errors.
## Problem
When context compaction slices messages with `messages[-KEEP_RECENT:]`,
a naive slice can separate an assistant message containing `tool_calls`
from its corresponding tool response messages. This causes API
validation errors like:
```
messages.0.content.1: unexpected 'tool_use_id' found in 'tool_result' blocks: orphan_12345.
Each 'tool_result' block must have a corresponding 'tool_use' block in the previous message.
```
## Solution
Added `_ensure_tool_pairs_intact()` helper function that:
1. Detects orphan tool responses in a slice (tool messages whose
`tool_call_id` has no matching assistant message)
2. Extends the slice backwards to include the missing assistant messages
3. Falls back to removing orphan tool responses if the assistant cannot
be found (edge case)
Applied this safeguard to:
- The initial `KEEP_RECENT` slice (line ~990)
- The progressive fallback slices when still over token limit (line
~1079)
## Testing
- Syntax validated with `python -m py_compile`
- Logic reviewed for correctness
## Linear
Fixes SECRT-1839
---
*Debugged by Toran & Orion in #agpt Discord*
## Summary
Agent generation (`create_agent`, `edit_agent`) can take 1-5 minutes.
Previously, if the user closed their browser tab during this time:
1. The SSE connection would die
2. The tool execution would be cancelled via `CancelledError`
3. The result would be lost - even if the agent-generator service
completed successfully
This PR ensures long-running tool operations survive SSE disconnections.
### Changes 🏗️
**Backend:**
- **base.py**: Added `is_long_running` property to `BaseTool` for tools
to opt-in to background execution
- **create_agent.py / edit_agent.py**: Set `is_long_running = True`
- **models.py**: Added `OperationStartedResponse`,
`OperationPendingResponse`, `OperationInProgressResponse` types
- **service.py**: Modified `_yield_tool_call()` to:
- Check if tool is `is_long_running`
- Save "pending" message to chat history immediately
- Spawn background task that runs independently of SSE
- Return `operation_started` immediately (don't wait)
- Update chat history with result when background task completes
- Track running operations for idempotency (prevents duplicate ops on
refresh)
- **db.py**: Added `update_tool_message_content()` to update pending
messages
- **model.py**: Added `invalidate_session_cache()` to clear Redis after
background completion
**Frontend:**
- **useChatMessage.ts**: Added operation message types
- **helpers.ts**: Handle `operation_started`, `operation_pending`,
`operation_in_progress` response types
- **PendingOperationWidget**: New component to display operation status
with spinner
- **ChatMessage.tsx**: Render `PendingOperationWidget` for operation
messages
### How It Works
```
User Request → Save "pending" message → Spawn background task → Return immediately
↓
Task runs independently of SSE
↓
On completion: Update message in chat history
↓
User refreshes → Loads history → Sees result
```
### User Experience
1. User requests agent creation
2. Sees "Agent creation started. You can close this tab - check your
library in a few minutes."
3. Can close browser tab safely
4. When they return, chat shows the completed result (or error)
### 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] pyright passes (0 errors)
- [x] TypeScript checks pass
- [x] Formatters applied
### Test Plan
1. Start agent creation in copilot
2. Close browser tab immediately after seeing "operation_started"
3. Wait 2-3 minutes
4. Reopen chat
5. Verify: Chat history shows completion message and agent appears in
library
---------
Co-authored-by: Ubbe <hi@ubbe.dev>
## Changes 🏗️
On the **Copilot** page:
- prevent unnecessary sidebar repaints
- show a disclaimer when switching chats on the sidebar to terminate a
current stream
- handle loading better
- save streams better when disconnecting
### 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 and test the above
## Summary
Disabled blocks (e.g., webhook blocks without `platform_base_url`
configured) were being indexed and returned in chat tool search results.
This PR ensures they are properly filtered out.
### Changes 🏗️
- **find_block.py**: Skip disabled blocks when enriching search results
- **content_handlers.py**:
- Skip disabled blocks during embedding indexing
- Update `get_stats()` to only count enabled blocks for accurate
coverage metrics
### Why
Blocks can be disabled for various reasons (missing OAuth config, no
platform URL for webhooks, etc.). These blocks shouldn't appear in
search results since users cannot use them.
### 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 disabled blocks are filtered from search results
- [x] Verified disabled blocks are not indexed
- [x] Verified stats accurately reflect enabled block count
## Changes 🏗️
- **Fix infinite loop in copilot page**
- use Zustand selectors instead of full store object to get stable
function references
- **Centralize chat streaming logic**
- move all streaming files from `providers/chat-stream/` to
`components/contextual/Chat/` for better colocation and reusability
- **Rename `copilot-store` → `copilot-page-store`**: Clarify scope
- **Fix message duplication**
- Only replay chunks from active streams (not completed ones) since
backend already provides persisted messages in `initialMessages`
- **Auto-focus chat input**
- Focus textarea when streaming ends and input is re-enabled
- **Graceful error display**
- Render tool response errors in muted style (small text + warning icon)
instead of raw "Error: ..." text
## 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] Navigate to copilot page - no infinite loop errors
- [x] Start a new chat, send message, verify streaming works
- [x] Navigate away and back to a completed session - no duplicate
messages
- [x] After stream completes, verify chat input receives focus
- [x] Trigger a tool error - verify it displays with muted styling
## Summary
- Fixes race condition when multiple concurrent requests try to process
the same reviews (e.g., double-click, multiple browser tabs)
- Previously the second request would fail with "Reviews not found,
access denied, or not in WAITING status"
- Now handles this gracefully by treating already-processed reviews with
the same decision as success
## Changes
- Added `get_reviews_by_node_exec_ids()` function that fetches reviews
regardless of status
- Modified `process_all_reviews_for_execution()` to handle
already-processed reviews
- Updated route to use idempotent validation
## Test plan
- [x] Linter passes (`poetry run ruff check`)
- [x] Type checker passes (`poetry run pyright`)
- [x] Formatter passes (`poetry run format`)
- [ ] Manual testing: double-click approve button should not cause
errors
Fixes AUTOGPT-SERVER-7HE
## Summary
Long-running chat tools (like `create_agent` and `edit_agent`) were
timing out because no SSE data was sent during tool execution. GCP load
balancers and proxies have idle connection timeouts (~60 seconds), and
when the external Agent Generator service takes longer than this, the
connection would drop.
This PR adds SSE heartbeat comments during tool execution to keep
connections alive.
### Changes 🏗️
- **response_model.py**: Added `StreamHeartbeat` response type that
emits SSE comments (`: heartbeat\n\n`)
- **service.py**: Modified `_yield_tool_call()` to:
- Run tool execution in a background asyncio task
- Yield heartbeat events every 15 seconds while waiting
- Handle task failures with explicit error responses (no silent
failures)
- Handle cancellation gracefully
- **create_agent.py**: Improved error messages with more context and
details
- **edit_agent.py**: Improved error messages with more context and
details
### How It Works
```
Tool Call → Background Task Started
│
├── Every 15 seconds: yield `: heartbeat\n\n` (SSE comment)
│
└── Task Complete → yield tool result OR error response
```
SSE comments (`: heartbeat\n\n`) are:
- Ignored by SSE clients (don't trigger events)
- Keep TCP connections alive through proxies/load balancers
- Don't affect the AI SDK data protocol
### 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 chat service tests pass (17 tests)
- [x] Verified heartbeats are sent during long tool execution
- [x] Verified errors are properly reported to frontend
## Changes 🏗️
Implements automatic context window management to prevent chat failures
when conversations exceed token limits.
### Problem
- **Issue**: [SECRT-1800] Long chat conversations stop working when
context grows beyond model limits (~113k tokens observed)
- **Root Cause**: Chat service sends ALL messages to LLM without
token-aware compression, eventually exceeding Claude Opus 4.5's 200k
context window
### Solution
Implements a sliding window with summarization strategy:
1. Monitors token count before sending to LLM (triggers at 120k tokens)
2. Keeps last 15 messages completely intact (preserves recent
conversation flow)
3. Summarizes older messages using gpt-4o-mini (fast & cheap)
4. Rebuilds context: `[system_prompt] + [summary] +
[recent_15_messages]`
5. Full history preserved in database (only compresses when sending to
LLM)
### Changes Made
- **Added** `_summarize_messages()` helper function to create concise
summaries using gpt-4o-mini
- **Modified** `_stream_chat_chunks()` to implement token counting and
conditional summarization
- **Integrated** existing `estimate_token_count()` utility for accurate
token measurement
- **Added** graceful fallback - continues with original messages if
summarization fails
## Motivation and Context 🎯
Without context management, users with long chat sessions (250+
messages) experience:
- Complete chat failure when hitting 200k token limit
- Lost conversation context
- Poor user experience
This fix enables:
- ✅ Unlimited conversation length
- ✅ Transparent operation (no UX changes)
- ✅ Preserved conversation quality (recent messages intact)
- ✅ Cost-efficient (~$0.0001 per summarization)
## Testing 🧪
### Expected Behavior
- Conversations < 120k tokens: No change (normal operation)
- Conversations > 120k tokens:
- Log message: `Context summarized: {tokens} tokens, kept last 15
messages + summary`
- Chat continues working smoothly
- Recent context remains intact
### How to Verify
1. Start a chat session in copilot
2. Send 250-600 messages (or 50+ with large code blocks)
3. Check logs for "Context summarized:" message
4. Verify chat continues working without errors
5. Verify conversation quality remains good
## Checklist ✅
- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my own code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] My changes generate no new warnings
- [x] I have tested my changes and verified they work as expected
We are removing Langfuse tracing from the chat/copilot system in favor
of using OpenRouter's broadcast feature, which keeps our codebase
simpler. Langfuse prompt management is retained for fetching system
prompts.
### Changes 🏗️
**Removed Langfuse tracing:**
- Removed `@observe` decorators from all 11 chat tool files
- Removed `langfuse.openai` wrapper (now using standard `openai` client)
- Removed `start_as_current_observation` and `propagate_attributes`
context managers from `service.py`
- Removed `update_current_trace()`, `update_current_span()`,
`span.update()` calls
**Retained Langfuse prompt management:**
- `langfuse.get_prompt()` for fetching system prompts
- `_is_langfuse_configured()` check for prompt availability
- Configuration for `langfuse_prompt_name`
**Files modified:**
- `backend/api/features/chat/service.py`
- `backend/api/features/chat/tools/*.py` (11 tool files)
### 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 `poetry run format` passes
- [x] Verified no `@observe` decorators remain in chat tools
- [x] Verified Langfuse prompt fetching is still functional (code
preserved)
## Summary
Add interactive UI to collect user answers when the agent-generator
service returns clarifying questions during agent creation/editing.
Previously, when the backend asked clarifying questions, the frontend
would just display them as text with no way for users to answer. This
caused the chat to keep retrying without the necessary context.
## Changes
- **ChatMessageData type**: Add `clarification_needed` variant with
questions field
- **ClarificationQuestionsWidget**: New component with interactive form
to collect answers
- **parseToolResponse**: Detect and parse `clarification_needed`
responses from backend
- **ChatMessage**: Render the widget when clarification is needed
## How It Works
1. User requests to create/edit agent
2. Backend returns `ClarificationNeededResponse` with list of questions
3. Frontend shows interactive form with text inputs for each question
4. User fills in answers and clicks "Submit Answers"
5. Answers are sent back as context to the tool
6. Backend receives full context and continues
## UI Features
- Shows all questions with examples (if provided)
- Input validation (all questions must be answered to submit)
- Visual feedback (checkmarks when answered)
- Numbered questions for clarity
- Submit button disabled until all answered
- Follows same design pattern as `credentials_needed` flow
## Related
- Backend support for clarification was added in #11819
- Fixes the issue shown in the screenshot where users couldn't answer
clarifying questions
## Test plan
- [ ] Test creating agent that requires clarifying questions
- [ ] Verify questions are displayed in interactive form
- [ ] Verify all questions must be answered before submitting
- [ ] Verify answers are sent back to backend as context
- [ ] Verify agent creation continues with full context
