Workflow
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
"""
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
"""
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
"""
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.