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Magnus Hedemark
6c193ae2d8
DSPy v1.1.0: validation audit, worked RAG compilation example, expand ref table Haystack v1.1.0: validation audit, file converters/YAML/component types, +2 refs CrewAI v1.1.0: validation audit, unified Memory system, Flows docs, +3 refs AutoGen v1.1.0: validation audit, v0.4 migration guide, AgentTool, streaming, +2 refs All API surfaces validated against official docs.
62 lines
1.6 KiB
Markdown
62 lines
1.6 KiB
Markdown
# CrewAI Flows — Event-Driven Orchestration
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Flows connect multiple Crews into event-driven workflows with state management, resumption, and conditional branching.
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## Basic Flow
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```python
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from crewai.flow.flow import Flow, listen, start
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class MyFlow(Flow):
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@start()
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def begin(self):
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print("Flow started")
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return {"data": "initial"}
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@listen(begin)
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def process_data(self, state):
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print(f"Processing: {state['data']}")
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# Launch a crew here
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return {"result": "processed"}
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flow = MyFlow()
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result = flow.kickoff()
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```
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## State Management with @persist
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```python
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from crewai.flow.flow import Flow, listen, start, persist
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@persist # State persists across executions
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class PersistentFlow(Flow):
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counter: int = 0 # Tracked state
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@start()
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def increment(self):
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self.counter += 1
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return {"counter": self.counter}
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```
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## Connecting Multiple Crews
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```python
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class ResearchFlow(Flow):
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@start()
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def research(self):
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crew = Crew(agents=[researcher], tasks=[research_task], process=Process.sequential)
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return crew.kickoff()
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@listen(research)
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def write_report(self, state):
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crew = Crew(agents=[writer], tasks=[write_task], process=Process.sequential)
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return crew.kickoff()
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```
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## Key Features
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- **Event-driven:** `@listen` decorator triggers on completion of upstream steps
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- **State management:** `@persist` enables state to survive across executions
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- **Restoration:** `restore_from_state_id` to resume flows from checkpoints
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- **Multiple crews:** Connect separate crews into a single orchestrated workflow
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