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docs/mkdocs.yml
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- SwarmRouter Example: "swarms/examples/swarm_router.md"
- MultiAgentRouter Minimal Example: "swarms/examples/multi_agent_router_minimal.md"
- ConcurrentWorkflow Example: "swarms/examples/concurrent_workflow.md"
- GraphWorkflow Example: "swarms/examples/graphworkflow_example.md"
- Multi-Agentic Patterns with GraphWorkflow: "swarms/examples/graphworkflow_rustworkx_patterns.md"
- Mixture of Agents Example: "swarms/examples/moa_example.md"
- LLM Council Examples: "examples/llm_council_examples.md"

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docs/swarms/examples/graphworkflow_example.md
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# GraphWorkflow Examples
This page provides simple, practical examples of how to use `GraphWorkflow` for various real-world scenarios.
## Basic Example: Research and Analysis Pipeline
```python
from swarms import Agent, GraphWorkflow
# Create specialized research and analysis agents
research_agent = Agent(
agent_name="ResearchAgent",
agent_description="Expert in research, data gathering, and information synthesis",
system_prompt="""You are a research specialist with expertise in:
- Gathering information from reliable sources
- Analyzing data and identifying key insights
- Synthesizing findings into clear summaries
- Verifying facts and checking sources
- Organizing information logically""",
model_name="gpt-4.1",
max_loops=1,
)
analysis_agent = Agent(
agent_name="AnalysisAgent",
agent_description="Specialist in data analysis and insight extraction",
system_prompt="""You are an analyst with expertise in:
- Processing research findings
- Extracting key insights and patterns
- Identifying trends and implications
- Preparing actionable recommendations""",
model_name="gpt-4.1",
max_loops=1,
)
synthesis_agent = Agent(
agent_name="SynthesisAgent",
agent_description="Expert in combining insights into cohesive reports",
system_prompt="""You are a synthesis specialist with expertise in:
- Combining insights from multiple sources
- Creating cohesive reports
- Ensuring consistency and clarity
- Highlighting key takeaways""",
model_name="gpt-4.1",
max_loops=1,
)
# Create workflow
workflow = GraphWorkflow(
name="Research-Analysis-Pipeline",
verbose=True
)
# Add agents as nodes
workflow.add_node(research_agent)
workflow.add_node(analysis_agent)
workflow.add_node(synthesis_agent)
# Connect agents sequentially
workflow.add_edge("ResearchAgent", "AnalysisAgent")
workflow.add_edge("AnalysisAgent", "SynthesisAgent")
# Execute workflow
task = "Research and analyze the latest trends in renewable energy technology"
result = workflow.run(task=task)
print(result)
```
## Parallel Analysis Example
```python
from swarms import Agent, GraphWorkflow
# Create specialized analysis agents
data_collector = Agent(
agent_name="DataCollector",
agent_description="Expert in collecting and organizing data from various sources",
system_prompt="You are a data collection specialist. Collect and organize data from various sources, ensuring accuracy and completeness.",
model_name="gpt-4.1",
max_loops=1,
)
technical_analyst = Agent(
agent_name="TechnicalAnalyst",
agent_description="Specialist in technical analysis and data processing",
system_prompt="You are a technical analyst. Perform technical analysis on data, identify patterns, and extract technical insights.",
model_name="gpt-4.1",
max_loops=1,
)
market_analyst = Agent(
agent_name="MarketAnalyst",
agent_description="Expert in market trends and competitive analysis",
system_prompt="You are a market analyst. Analyze market trends, competitive landscape, and market conditions.",
model_name="gpt-4.1",
max_loops=1,
)
synthesis_agent = Agent(
agent_name="SynthesisAgent",
agent_description="Expert in synthesizing multiple perspectives into cohesive reports",
system_prompt="You are a synthesis specialist. Combine insights from multiple analysts into a cohesive, comprehensive report.",
model_name="gpt-4.1",
max_loops=1,
)
# Create workflow
workflow = GraphWorkflow(
name="Parallel-Analysis-Pipeline",
verbose=True
)
# Add all agents
for agent in [data_collector, technical_analyst, market_analyst, synthesis_agent]:
workflow.add_node(agent)
# Fan-out: one agent feeds multiple agents (parallel execution)
workflow.add_edges_from_source(
"DataCollector",
["TechnicalAnalyst", "MarketAnalyst"]
)
# Fan-in: multiple agents feed one agent
workflow.add_edges_to_target(
["TechnicalAnalyst", "MarketAnalyst"],
"SynthesisAgent"
)
# Execute workflow
task = "Analyze Bitcoin market trends for Q4 2024"
result = workflow.run(task=task)
print(result)
```
## Content Creation Pipeline Example
```python
from swarms import Agent, GraphWorkflow
# Create content creation agents
researcher = Agent(
agent_name="Researcher",
agent_description="Expert in comprehensive research and information gathering",
system_prompt="""You are a research specialist. Conduct thorough research on the given topic,
gather information from multiple sources, and provide comprehensive findings with citations.""",
model_name="gpt-4.1",
max_loops=1,
)
outline_creator = Agent(
agent_name="OutlineCreator",
agent_description="Specialist in creating structured content outlines",
system_prompt="""You are an outline specialist. Create detailed outlines based on research findings.
Structure content logically with clear sections and subsections.""",
model_name="gpt-4.1",
max_loops=1,
)
writer = Agent(
agent_name="Writer",
agent_description="Professional writer specializing in engaging content creation",
system_prompt="""You are a professional writer. Write engaging content based on the outline and research.
Ensure clarity, proper grammar, and engaging style.""",
model_name="gpt-4.1",
max_loops=1,
)
editor = Agent(
agent_name="Editor",
agent_description="Expert editor specializing in content review and refinement",
system_prompt="""You are an expert editor. Review content for accuracy, clarity, grammar, and style.
Ensure consistency and professional quality.""",
model_name="gpt-4.1",
max_loops=1,
)
# Create workflow
workflow = GraphWorkflow(
name="Content-Creation-Pipeline",
verbose=True
)
# Add agents
for agent in [researcher, outline_creator, writer, editor]:
workflow.add_node(agent)
# Create sequential flow
workflow.add_edge("Researcher", "OutlineCreator")
workflow.add_edge("OutlineCreator", "Writer")
workflow.add_edge("Writer", "Editor")
# Execute content creation
task = "Create a comprehensive guide on implementing machine learning in production systems"
result = workflow.run(task=task)
print(result)
```
## Multi-Perspective Analysis Example
```python
from swarms import Agent, GraphWorkflow
# Create analysis agents with different perspectives
technical_analyst = Agent(
agent_name="TechnicalAnalyst",
agent_description="Expert in technical analysis focusing on implementation details",
system_prompt="Analyze from a technical perspective focusing on implementation details, architecture, and technical feasibility.",
model_name="gpt-4.1",
max_loops=1,
)
business_analyst = Agent(
agent_name="BusinessAnalyst",
agent_description="Specialist in business analysis focusing on ROI and market impact",
system_prompt="Analyze from a business perspective focusing on ROI, market impact, revenue potential, and business strategy.",
model_name="gpt-4.1",
max_loops=1,
)
risk_analyst = Agent(
agent_name="RiskAnalyst",
agent_description="Expert in risk assessment and mitigation strategies",
system_prompt="Analyze from a risk perspective focusing on potential issues, threats, and mitigation strategies.",
model_name="gpt-4.1",
max_loops=1,
)
synthesizer = Agent(
agent_name="Synthesizer",
agent_description="Expert in synthesizing multiple perspectives into comprehensive reports",
system_prompt="Synthesize all analysis perspectives into a comprehensive report that integrates technical, business, and risk considerations.",
model_name="gpt-4.1",
max_loops=1,
)
# Create workflow with rustworkx backend for better performance
workflow = GraphWorkflow(
name="Multi-Perspective-Analysis",
backend="rustworkx",
verbose=True
)
# Add agents
for agent in [technical_analyst, business_analyst, risk_analyst, synthesizer]:
workflow.add_node(agent)
# Parallel analysis followed by synthesis
workflow.add_edges_to_target(
["TechnicalAnalyst", "BusinessAnalyst", "RiskAnalyst"],
"Synthesizer"
)
# Execute workflow
task = "Analyze the adoption of blockchain technology in financial services"
result = workflow.run(task=task)
print(result)
```
## Using from_spec for Quick Setup
```python
from swarms import Agent, GraphWorkflow
# Create agents
researcher = Agent(
agent_name="Researcher",
agent_description="Research specialist",
system_prompt="Research the topic thoroughly and provide comprehensive findings",
model_name="gpt-4o-mini",
max_loops=1
)
analyzer = Agent(
agent_name="Analyzer",
agent_description="Data analyst",
system_prompt="Analyze research findings and extract key insights",
model_name="gpt-4o-mini",
max_loops=1
)
reporter = Agent(
agent_name="Reporter",
agent_description="Report writer",
system_prompt="Create final report based on analysis",
model_name="gpt-4o-mini",
max_loops=1
)
# Create workflow from specification
workflow = GraphWorkflow.from_spec(
agents=[researcher, analyzer, reporter],
edges=[
("Researcher", "Analyzer"),
("Analyzer", "Reporter"),
],
task="Analyze climate change data and create a report",
backend="rustworkx"
)
# Execute workflow
results = workflow.run()
print(results)
```
## Batch Processing
```python
from swarms import Agent, GraphWorkflow
# Create analysis agents
market_agent = Agent(
agent_name="MarketAnalyst",
agent_description="Expert in market analysis and trends",
model_name="gpt-4.1",
max_loops=1,
)
technical_agent = Agent(
agent_name="TechnicalAnalyst",
agent_description="Specialist in technical analysis and patterns",
model_name="gpt-4.1",
max_loops=1,
)
synthesis_agent = Agent(
agent_name="SynthesisAgent",
agent_description="Expert in synthesizing analysis results",
model_name="gpt-4.1",
max_loops=1,
)
# Create workflow
workflow = GraphWorkflow(
name="Analysis-Pipeline",
verbose=True
)
# Add agents
for agent in [market_agent, technical_agent, synthesis_agent]:
workflow.add_node(agent)
# Create edges
workflow.add_edges_to_target(
["MarketAnalyst", "TechnicalAnalyst"],
"SynthesisAgent"
)
# Execute multiple tasks
tasks = [
"Analyze Apple (AAPL) stock performance",
"Evaluate Microsoft (MSFT) market position",
"Assess Google (GOOGL) competitive landscape"
]
results = workflow.batch_run(tasks=tasks)
for i, result in enumerate(results):
print(f"Task {i+1} Result:", result)
```
## Visualizing Workflow Structure
You can visualize the structure of your workflow before executing tasks using the `visualize()` method:
```python
from swarms import Agent, GraphWorkflow
# Create specialized agents
research_agent = Agent(
agent_name="ResearchAgent",
agent_description="Specialized in comprehensive research and data gathering",
model_name="gpt-4o-mini",
max_loops=1,
)
analysis_agent = Agent(
agent_name="AnalysisAgent",
agent_description="Expert in data analysis and pattern recognition",
model_name="gpt-4o-mini",
max_loops=1,
)
synthesis_agent = Agent(
agent_name="SynthesisAgent",
agent_description="Specialized in synthesizing insights into reports",
model_name="gpt-4o-mini",
max_loops=1,
)
# Create workflow
workflow = GraphWorkflow(
name="Research-Pipeline",
verbose=True
)
# Add agents
for agent in [research_agent, analysis_agent, synthesis_agent]:
workflow.add_node(agent)
# Create edges
workflow.add_edge("ResearchAgent", "AnalysisAgent")
workflow.add_edge("AnalysisAgent", "SynthesisAgent")
# Visualize the workflow structure
# Requires graphviz: pip install graphviz
output_file = workflow.visualize(
format="png",
view=True,
show_summary=True
)
print(f"Visualization saved to: {output_file}")
# Or use simple text visualization
text_viz = workflow.visualize_simple()
print(text_viz)
```
## Saving and Loading Workflows
You can save workflows for reuse and load them later:
```python
from swarms import Agent, GraphWorkflow
# Create and configure workflow
workflow = GraphWorkflow(name="My-Workflow")
# ... add agents and edges ...
# Save workflow with conversation history
workflow.save_to_file(
"my_workflow.json",
include_conversation=True,
include_runtime_state=True
)
# Load workflow later
loaded_workflow = GraphWorkflow.load_from_file(
"my_workflow.json",
restore_runtime_state=True
)
# Continue execution
results = loaded_workflow.run("Follow-up analysis")
```
## Key Takeaways
1. **Node-Based Architecture**: Agents are nodes, edges define data flow and execution order
2. **Parallel Execution**: Use fan-out patterns (`add_edges_from_source`) for parallel processing
3. **Backend Selection**: Use `backend="rustworkx"` for large workflows (1000+ nodes) for 5-10x better performance
4. **Automatic Compilation**: Workflows are automatically compiled for optimal execution layers
5. **Visualization**: Use `visualize()` to see your workflow structure before execution
6. **Persistence**: Save and load workflows for reuse with `save_to_file()` and `load_from_file()`
7. **Quick Setup**: Use `from_spec()` for rapid workflow creation from agent and edge specifications
For more detailed information about the `GraphWorkflow` API and advanced usage patterns, see the [main documentation](../structs/graph_workflow.md).
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