ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/gaia-experience/AWorld/docs/Quickstart/workflow_construction.md

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+
+We use the classic graph syntax to describe workflows in AWorld. 
+The following are the basic scenarios for constructing agent workflows.
+
+## Agent Native Workflow
+
+### Sequential
+```python
+"""
+Sequential Agent Pipeline: agent1 → agent2 → agent3
+
+Executes agents in sequence where each agent's output becomes 
+the next agent's input, enabling multi-step collaborative processing.
+"""
+
+swarm = Swarm([(agent1, agent2), (agent2, agent3)], root_agent=[agent1])
+result: TaskResponse = Runners.run(input=question, swarm=swarm)
+```
+
+### Parallel
+```python
+"""
+Parallel Agent Execution with Barrier Synchronization
+
+    Input ──┬─→ agent1 ──┐
+            │            ├──→ agent3 (barrier wait)
+            └─→ agent2 ──┘
+
+- agent1 and agent2 execute in parallel
+- agent3 acts as a barrier, waiting for both agents
+- agent3 processes combined outputs from agent1 and agent2
+"""
+
+swarm = Swarm([(agent1, agent3), (agent2, agent3)], root_agent=[agent1, agent2])
+result: TaskResponse = Runners.run(input=question, swarm=swarm)
+```
+
+### Parallel Multi-Path 
+```python
+"""
+Parallel Multi-Path Agent Execution
+
+    Input ──→ agent1 ──┬──→ agent2 ──┐
+                       │             │
+                       └──→ agent3 ←─┘ (barrier wait for agent1 & agent2)
+
+- Single input enters only through agent1
+- agent1 distributes to both agent2 and agent3
+- agent2 processes and feeds agent3
+- agent3 waits for both agent1 and agent2 completion
+- agent3 synthesizes outputs from both agent1 and agent2
+"""
+
+swarm = Swarm([(agent1, agent2), (agent1, agent3), (agent2, agent3)], root_agent=[agent1])
+result: TaskResponse = Runners.run(input=question, swarm=swarm)
+```
+
+## Task Native Workflow
+Task native workflow is further implemented for Isolating the agent runtimes and environments, 
+in the distributed or other easy-to-overlap scenarios. 
+Task native workflow is further implemented for isolating agent runtimes and environments, 
+particularly useful in distributed or other scenarios where tool-isolation is required. 
+```python
+task1 = Task(input="my question", agent=agent1)
+task2 = Task(agent=agent2)
+task3 = Task(agent=agent3)
+tasks = [task1, task2, task3]
+
+result: Dict[str, TaskResponse] = Runners.run_task(tasks, RunConfig(sequence_dependent=True))
+```