[autofix.ci] apply automated fixes

api/core/workflow/docs/WORKER_POOL_CONFIG.md

@@ -5,9 +5,9 @@
 The GraphEngine now supports **dynamic worker pool management** to optimize performance and resource usage. Instead of a fixed 10-worker pool, the engine can:
 
 1. **Start with optimal worker count** based on graph complexity
-2. **Scale up** when workload increases
-3. **Scale down** when workers are idle
-4. **Respect configurable min/max limits**
+1. **Scale up** when workload increases
+1. **Scale down** when workers are idle
+1. **Respect configurable min/max limits**
 
 ## Benefits
 
@@ -60,7 +60,7 @@ export GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME=10.0
 The engine analyzes the graph structure at startup:
 
 - **Sequential graphs** (no branches): 1 worker
-- **Limited parallelism** (few branches): 2 workers  
+- **Limited parallelism** (few branches): 2 workers
 - **Moderate parallelism**: 3 workers
 - **High parallelism** (many branches): 5 workers
 
@@ -69,11 +69,13 @@ The engine analyzes the graph structure at startup:
 During execution:
 
 1. **Scale Up** triggers when:
+
    - Queue depth exceeds `SCALE_UP_THRESHOLD`
    - All workers are busy and queue has items
    - Not at `MAX_WORKERS` limit
 
-2. **Scale Down** triggers when:
+1. **Scale Down** triggers when:
+
    - Worker idle for more than `SCALE_DOWN_IDLE_TIME` seconds
    - Above `MIN_WORKERS` limit
 
@@ -146,11 +148,11 @@ INFO: Scaled down workers: 3 -> 2 (removed 1 idle workers)
 ## Best Practices
 
 1. **Start with defaults** - They work well for most cases
-2. **Monitor queue depth** - Adjust `SCALE_UP_THRESHOLD` if queues back up
-3. **Consider workload patterns**:
+1. **Monitor queue depth** - Adjust `SCALE_UP_THRESHOLD` if queues back up
+1. **Consider workload patterns**:
    - Bursty: Lower `SCALE_DOWN_IDLE_TIME`
    - Steady: Higher `SCALE_DOWN_IDLE_TIME`
-4. **Test with your workloads** - Measure and tune
+1. **Test with your workloads** - Measure and tune
 
 ## Troubleshooting
 

api/core/workflow/graph_engine/README.md

@@ -147,9 +147,9 @@ classDiagram
 ### Data Flow
 
 1. **Commands** flow from CommandChannels → CommandProcessing → Domain
-2. **Events** flow from Workers → EventHandlerRegistry → State updates
-3. **Node outputs** flow from Workers → OutputRegistry → ResponseCoordinator
-4. **Ready nodes** flow from GraphTraversal → StateManagement → WorkerManagement
+1. **Events** flow from Workers → EventHandlerRegistry → State updates
+1. **Node outputs** flow from Workers → OutputRegistry → ResponseCoordinator
+1. **Ready nodes** flow from GraphTraversal → StateManagement → WorkerManagement
 
 ### Extension Points
 
@@ -160,11 +160,11 @@ classDiagram
 ## Execution Flow
 
 1. **Initialization**: GraphEngine creates all subsystems with the workflow graph
-2. **Node Discovery**: Traversal components identify ready nodes
-3. **Worker Execution**: Workers pull from ready queue and execute nodes
-4. **Event Processing**: Dispatcher routes events to appropriate handlers
-5. **State Updates**: Managers track node/edge states for next steps
-6. **Completion**: Coordinator detects when all nodes are done
+1. **Node Discovery**: Traversal components identify ready nodes
+1. **Worker Execution**: Workers pull from ready queue and execute nodes
+1. **Event Processing**: Dispatcher routes events to appropriate handlers
+1. **State Updates**: Managers track node/edge states for next steps
+1. **Completion**: Coordinator detects when all nodes are done
 
 ## Usage
 

api/tests/unit_tests/core/workflow/graph_engine/README.md

@@ -5,7 +5,7 @@
 This directory contains a comprehensive testing framework for the Graph Engine, including:
 
 1. **TableTestRunner** - Advanced table-driven test framework for workflow testing
-2. **Auto-Mock System** - Powerful mocking framework for testing without external dependencies
+1. **Auto-Mock System** - Powerful mocking framework for testing without external dependencies
 
 ## TableTestRunner Framework
 
@@ -210,7 +210,7 @@ result = runner.run_test_case(test_case)
 The auto-mock system provides a powerful framework for testing workflows that contain nodes requiring third-party services (LLM, APIs, tools, etc.) without making actual external calls. This enables:
 
 - **Fast test execution** - No network latency or API rate limits
-- **Deterministic results** - Consistent outputs for reliable testing  
+- **Deterministic results** - Consistent outputs for reliable testing
 - **Cost savings** - No API usage charges during testing
 - **Offline testing** - Tests can run without internet connectivity
 - **Error simulation** - Test error handling without triggering real failures
@@ -400,11 +400,11 @@ Use `TableTestRunner` to execute test cases and validate results.
 ## Best Practices
 
 1. **Use descriptive mock responses** - Make it clear in outputs that they are mocked
-2. **Test both success and failure paths** - Use error simulation to test error handling
-3. **Keep mock configs close to tests** - Define mocks in the same test file for clarity
-4. **Use custom handlers sparingly** - Only when dynamic behavior is needed
-5. **Document mock behavior** - Comment why specific mock values are chosen
-6. **Validate mock accuracy** - Ensure mocks reflect real service behavior
+1. **Test both success and failure paths** - Use error simulation to test error handling
+1. **Keep mock configs close to tests** - Define mocks in the same test file for clarity
+1. **Use custom handlers sparingly** - Only when dynamic behavior is needed
+1. **Document mock behavior** - Comment why specific mock values are chosen
+1. **Validate mock accuracy** - Ensure mocks reflect real service behavior
 
 ## Examples
 
@@ -481,7 +481,7 @@ uv run pytest api/tests/unit_tests/core/workflow/graph_engine/ -n auto
 Potential improvements to the auto-mock system:
 
 1. **Recording and playback** - Record real API responses for replay in tests
-2. **Mock templates** - Pre-defined mock configurations for common scenarios
-3. **Async support** - Better support for async node execution
-4. **Mock validation** - Validate mock outputs against node schemas
-5. **Performance profiling** - Built-in performance metrics for mocked workflows
+1. **Mock templates** - Pre-defined mock configurations for common scenarios
+1. **Async support** - Better support for async node execution
+1. **Mock validation** - Validate mock outputs against node schemas
+1. **Performance profiling** - Built-in performance metrics for mocked workflows