Workflow Agents
Multi-agent orchestration patterns for complex task pipelines: Sequential, Parallel, and Router agents.Sequential Agent
Run agents in order where each agent’s output feeds into the next:from omnicoreagent import OmniCoreAgent, SequentialAgent
# Define specialized agents
researcher = OmniCoreAgent(
name="researcher",
system_instruction="Research the given topic thoroughly.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
writer = OmniCoreAgent(
name="writer",
system_instruction="Write a report based on research findings.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
reviewer = OmniCoreAgent(
name="reviewer",
system_instruction="Review and improve the report.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
# Chain them together
pipeline = SequentialAgent(
sub_agents=[researcher, writer, reviewer],
model_config={"provider": "openai", "model": "gpt-4o"}
)
result = await pipeline.run(task="Write a report on quantum computing")
Parallel Agent
Run multiple agents simultaneously for independent tasks:from omnicoreagent import OmniCoreAgent, ParallelAgent
code_agent = OmniCoreAgent(
name="code_reviewer",
system_instruction="Review code for bugs.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
security_agent = OmniCoreAgent(
name="security_reviewer",
system_instruction="Check for security vulnerabilities.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
perf_agent = OmniCoreAgent(
name="performance_reviewer",
system_instruction="Identify performance issues.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
# Run all three simultaneously
parallel = ParallelAgent(
sub_agents=[code_agent, security_agent, perf_agent],
model_config={"provider": "openai", "model": "gpt-4o"}
)
result = await parallel.run(task="Review this codebase")
Router Agent
Intelligently route tasks to the best-suited specialist:from omnicoreagent import OmniCoreAgent, RouterAgent
code_agent = OmniCoreAgent(
name="code_expert",
system_instruction="You handle coding tasks.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
data_agent = OmniCoreAgent(
name="data_expert",
system_instruction="You handle data analysis.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
research_agent = OmniCoreAgent(
name="research_expert",
system_instruction="You handle research tasks.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
# Router: Intelligent task routing
router = RouterAgent(
sub_agents=[code_agent, data_agent, research_agent],
model_config={"provider": "openai", "model": "gpt-4o"}
)
result = await router.run(task="Find and summarize AI research")
When to Use Each Pattern
| Pattern | Best For | Example |
|---|---|---|
| SequentialAgent | Tasks that depend on each other (output of one → input of next) | Research → Write → Review |
| ParallelAgent | Independent tasks that can run simultaneously for speed | Code + Security + Performance review |
| RouterAgent | Intelligent task routing to specialized agents | Route “analyze sales” to data agent |
Combine patterns for complex workflows. For example, use a Router to select a pipeline, which then runs a Sequential or Parallel agent.