dify
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dify/api/core/workflow/README.md
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dify/api/core/workflow/README.md
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# Workflow
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## Project Overview
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This is the workflow graph engine module of Dify, implementing a queue-based distributed workflow execution system. The engine handles agentic AI workflows with support for parallel execution, node iteration, conditional logic, and external command control.
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## Architecture
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### Core Components
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The graph engine follows a layered architecture with strict dependency rules:
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1. **Graph Engine** (`graph_engine/`) - Orchestrates workflow execution
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- **Manager** - External control interface for stop/pause/resume commands
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- **Worker** - Node execution runtime
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- **Command Processing** - Handles control commands (abort, pause, resume)
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- **Event Management** - Event propagation and layer notifications
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- **Graph Traversal** - Edge processing and skip propagation
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- **Response Coordinator** - Path tracking and session management
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- **Layers** - Pluggable middleware (debug logging, execution limits)
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- **Command Channels** - Communication channels (InMemory, Redis)
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1. **Graph** (`graph/`) - Graph structure and runtime state
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- **Graph Template** - Workflow definition
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- **Edge** - Node connections with conditions
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- **Runtime State Protocol** - State management interface
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1. **Nodes** (`nodes/`) - Node implementations
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- **Base** - Abstract node classes and variable parsing
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- **Specific Nodes** - LLM, Agent, Code, HTTP Request, Iteration, Loop, etc.
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1. **Events** (`node_events/`) - Event system
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- **Base** - Event protocols
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- **Node Events** - Node lifecycle events
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1. **Entities** (`entities/`) - Domain models
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- **Variable Pool** - Variable storage
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- **Graph Init Params** - Initialization configuration
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## Key Design Patterns
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### Command Channel Pattern
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External workflow control via Redis or in-memory channels:
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```python
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# Send stop command to running workflow
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channel = RedisChannel(redis_client, f"workflow:{task_id}:commands")
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channel.send_command(AbortCommand(reason="User requested"))
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```
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### Layer System
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Extensible middleware for cross-cutting concerns:
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```python
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engine = GraphEngine(graph)
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engine.layer(DebugLoggingLayer(level="INFO"))
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engine.layer(ExecutionLimitsLayer(max_nodes=100))
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```
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### Event-Driven Architecture
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All node executions emit events for monitoring and integration:
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- `NodeRunStartedEvent` - Node execution begins
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- `NodeRunSucceededEvent` - Node completes successfully
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- `NodeRunFailedEvent` - Node encounters error
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- `GraphRunStartedEvent/GraphRunCompletedEvent` - Workflow lifecycle
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### Variable Pool
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Centralized variable storage with namespace isolation:
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```python
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# Variables scoped by node_id
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pool.add(["node1", "output"], value)
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result = pool.get(["node1", "output"])
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```
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## Import Architecture Rules
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The codebase enforces strict layering via import-linter:
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1. **Workflow Layers** (top to bottom):
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- graph_engine → graph_events → graph → nodes → node_events → entities
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1. **Graph Engine Internal Layers**:
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- orchestration → command_processing → event_management → graph_traversal → domain
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1. **Domain Isolation**:
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- Domain models cannot import from infrastructure layers
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1. **Command Channel Independence**:
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- InMemory and Redis channels must remain independent
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## Common Tasks
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### Adding a New Node Type
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1. Create node class in `nodes/<node_type>/`
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1. Inherit from `BaseNode` or appropriate base class
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1. Implement `_run()` method
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1. Register in `nodes/node_mapping.py`
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1. Add tests in `tests/unit_tests/core/workflow/nodes/`
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### Implementing a Custom Layer
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1. Create class inheriting from `Layer` base
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1. Override lifecycle methods: `on_graph_start()`, `on_event()`, `on_graph_end()`
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1. Add to engine via `engine.layer()`
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### Debugging Workflow Execution
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Enable debug logging layer:
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```python
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debug_layer = DebugLoggingLayer(
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level="DEBUG",
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include_inputs=True,
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include_outputs=True
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)
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```
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