complete_stream previously opened a fresh db_session() per yielded
event, doing one Postgres INSERT + commit per chunk on the WSGI
thread. Streaming answers emit ~100s of answer chunks per response,
so the route was paying ~100 PG roundtrips per stream serialized on
commit latency.
New BatchedJournalWriter in application/streaming/message_journal.py
accumulates rows per stream and flushes on three triggers:
- size: buffer reaches 16 entries
- time: 100ms elapsed since the last flush
- lifecycle: close() at end-of-stream
Live pubsub publishes still fire synchronously per record(), so
subscribers see events in real time — only the durable journal write
is amortized. On bulk INSERT IntegrityError the writer falls back to
per-row record() with the existing seq+1 retry so a single colliding
seq doesn't drop the rest of the batch.
complete_stream wires journal_writer.close() into every exit path
(happy end, tool-approval-paused end, GeneratorExit, error handler)
so the terminal event is committed before the generator returns —
otherwise a reconnecting client could snapshot up to the last flush
boundary and live-tail waiting for an end that's still in memory.
Repository gets bulk_record() — one SQLAlchemy executemany INSERT
for the bulk path. All-or-nothing on collision (Postgres aborts the
whole batch); the writer's per-row fallback handles recovery.
* feat: implement WorkflowAgent and GraphExecutor for workflow management and execution
* refactor: workflow schemas and introduce WorkflowEngine
- Updated schemas in `schemas.py` to include new agent types and configurations.
- Created `WorkflowEngine` class in `workflow_engine.py` to manage workflow execution.
- Enhanced `StreamProcessor` to handle workflow-related data.
- Added new routes and utilities for managing workflows in the user API.
- Implemented validation and serialization functions for workflows.
- Established MongoDB collections and indexes for workflows and related entities.
* refactor: improve WorkflowAgent documentation and update type hints in WorkflowEngine
* feat: workflow builder and managing in frontend
- Added new endpoints for workflows in `endpoints.ts`.
- Implemented `getWorkflow`, `createWorkflow`, and `updateWorkflow` methods in `userService.ts`.
- Introduced new UI components for alerts, buttons, commands, dialogs, multi-select, popovers, and selects.
- Enhanced styling in `index.css` with new theme variables and animations.
- Refactored modal components for better layout and styling.
- Configured TypeScript paths and Vite aliases for cleaner imports.
* feat: add workflow preview component and related state management
- Implemented WorkflowPreview component for displaying workflow execution.
- Created WorkflowPreviewSlice for managing workflow preview state, including queries and execution steps.
- Added WorkflowMiniMap for visual representation of workflow nodes and their statuses.
- Integrated conversation handling with the ability to fetch answers and manage query states.
- Introduced reusable Sheet component for UI overlays.
- Updated Redux store to include workflowPreview reducer.
* feat: enhance workflow execution details and state management in WorkflowEngine and WorkflowPreview
* feat: enhance workflow components with improved UI and functionality
- Updated WorkflowPreview to allow text truncation for better display of long names.
- Enhanced BaseNode with connectable handles and improved styling for better visibility.
- Added MobileBlocker component to inform users about desktop requirements for the Workflow Builder.
- Introduced PromptTextArea component for improved variable insertion and search functionality, including upstream variable extraction and context addition.
* feat(workflow): add owner validation and graph version support
* fix: ruff lint
---------
Co-authored-by: Alex <a@tushynski.me>
* feat: Implement model registry and capabilities for multi-provider support
- Added ModelRegistry to manage available models and their capabilities.
- Introduced ModelProvider enum for different LLM providers.
- Created ModelCapabilities dataclass to define model features.
- Implemented methods to load models based on API keys and settings.
- Added utility functions for model management in model_utils.py.
- Updated settings.py to include provider-specific API keys.
- Refactored LLM classes (Anthropic, OpenAI, Google, etc.) to utilize new model registry.
- Enhanced utility functions to handle token limits and model validation.
- Improved code structure and logging for better maintainability.
* feat: Add model selection feature with API integration and UI component
* feat: Add model selection and default model functionality in agent management
* test: Update assertions and formatting in stream processing tests
* refactor(llm): Standardize model identifier to model_id
* fix tests
---------
Co-authored-by: Alex <a@tushynski.me>
* Update routes.py, added token and request limits to create/update agent operations
* added usage limit check to api endpoints
cannot create agents with usage limit right now that will be implemented
* implemented api limiting as either token limiting or request limiting modes
* minor typo & bug fix
