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@ -453,8 +453,8 @@ agent.run(task, img)
---- ----
### `ToolAgent` ### Local Agent `ToolAgent`
ToolAgent is an agent that can use tools through JSON function calling. It intakes any open source model from huggingface and is extremely modular and plug in and play. We need help adding general support to all models soon. ToolAgent is an fully local agent that can use tools through JSON function calling. It intakes any open source model from huggingface and is extremely modular and plug in and play. We need help adding general support to all models soon.
```python ```python
@ -774,6 +774,8 @@ print(
The `AgentRearrange` orchestration technique, inspired by Einops and einsum, allows you to define and map out the relationships between various agents. It provides a powerful tool for orchestrating complex workflows, enabling you to specify linear and sequential relationships such as `a -> a1 -> a2 -> a3`, or concurrent relationships where the first agent sends a message to 3 agents simultaneously: `a -> a1, a2, a3`. This level of customization allows for the creation of highly efficient and dynamic workflows, where agents can work in parallel or in sequence as needed. The `AgentRearrange` technique is a valuable addition to the swarms library, providing a new level of flexibility and control over the orchestration of agents. For more detailed information and examples, please refer to the [official documentation](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/). The `AgentRearrange` orchestration technique, inspired by Einops and einsum, allows you to define and map out the relationships between various agents. It provides a powerful tool for orchestrating complex workflows, enabling you to specify linear and sequential relationships such as `a -> a1 -> a2 -> a3`, or concurrent relationships where the first agent sends a message to 3 agents simultaneously: `a -> a1, a2, a3`. This level of customization allows for the creation of highly efficient and dynamic workflows, where agents can work in parallel or in sequence as needed. The `AgentRearrange` technique is a valuable addition to the swarms library, providing a new level of flexibility and control over the orchestration of agents. For more detailed information and examples, please refer to the [official documentation](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/).
[Check out my video on agent rearrange!](https://youtu.be/Rq8wWQ073mg)
### Methods ### Methods
@ -799,68 +801,184 @@ The `run` method returns the final output after all agents have processed the in
```python ```python
from swarms import Agent, AgentRearrange from datetime import datetime
# Initialize the director agent from swarms import Agent, AgentRearrange, create_file_in_folder
director = Agent( chief_medical_officer = Agent(
agent_name="Director", agent_name="Chief Medical Officer",
system_prompt="Directs the tasks for the workers", system_prompt="""You are the Chief Medical Officer coordinating a team of medical specialists for viral disease diagnosis.
model_name="claude-2", Your responsibilities include:
- Gathering initial patient symptoms and medical history
- Coordinating with specialists to form differential diagnoses
- Synthesizing different specialist opinions into a cohesive diagnosis
- Ensuring all relevant symptoms and test results are considered
- Making final diagnostic recommendations
- Suggesting treatment plans based on team input
- Identifying when additional specialists need to be consulted
Guidelines:
1. Always start with a comprehensive patient history
2. Consider both common and rare viral conditions
3. Factor in patient demographics and risk factors
4. Document your reasoning process clearly
5. Highlight any critical or emergency symptoms
6. Note any limitations or uncertainties in the diagnosis
Format all responses with clear sections for:
- Initial Assessment
- Differential Diagnoses
- Specialist Consultations Needed
- Recommended Next Steps""",
model_name="gpt-4o", # Models from litellm -> claude-2
max_loops=1, max_loops=1,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="director.json",
) )
# Viral Disease Specialist
virologist = Agent(
agent_name="Virologist",
system_prompt="""You are a specialist in viral diseases with expertise in:
- Respiratory viruses (Influenza, Coronavirus, RSV)
- Systemic viral infections (EBV, CMV, HIV)
- Childhood viral diseases (Measles, Mumps, Rubella)
- Emerging viral threats
Your role involves:
1. Analyzing symptoms specific to viral infections
2. Distinguishing between different viral pathogens
3. Assessing viral infection patterns and progression
4. Recommending specific viral tests
5. Evaluating epidemiological factors
For each case, consider:
- Incubation periods
- Transmission patterns
- Seasonal factors
- Geographic prevalence
- Patient immune status
- Current viral outbreaks
Provide detailed analysis of:
- Characteristic viral symptoms
- Disease progression timeline
- Risk factors for severe disease
- Potential complications""",
model_name="gpt-4o",
max_loops=1,
)
# Initialize worker 1 # Internal Medicine Specialist
internist = Agent(
agent_name="Internist",
system_prompt="""You are an Internal Medicine specialist responsible for:
- Comprehensive system-based evaluation
- Integration of symptoms across organ systems
- Identification of systemic manifestations
- Assessment of comorbidities
For each case, analyze:
1. Vital signs and their implications
2. System-by-system review (cardiovascular, respiratory, etc.)
3. Impact of existing medical conditions
4. Medication interactions and contraindications
5. Risk stratification
Consider these aspects:
- Age-related factors
- Chronic disease impact
- Medication history
- Social and environmental factors
Document:
- Physical examination findings
- System-specific symptoms
- Relevant lab abnormalities
- Risk factors for complications""",
model_name="gpt-4o",
max_loops=1,
)
worker1 = Agent( # Diagnostic Synthesizer
agent_name="Worker1", synthesizer = Agent(
system_prompt="Generates a transcript for a youtube video on what swarms are", agent_name="Diagnostic Synthesizer",
model_name="claude-2", system_prompt="""You are responsible for synthesizing all specialist inputs to create a final diagnostic assessment:
Core responsibilities:
1. Integrate findings from all specialists
2. Identify patterns and correlations
3. Resolve conflicting opinions
4. Generate probability-ranked differential diagnoses
5. Recommend additional testing if needed
Analysis framework:
- Weight evidence based on reliability and specificity
- Consider epidemiological factors
- Evaluate diagnostic certainty
- Account for test limitations
Provide structured output including:
1. Primary diagnosis with confidence level
2. Supporting evidence summary
3. Alternative diagnoses to consider
4. Recommended confirmatory tests
5. Red flags or warning signs
6. Follow-up recommendations
Documentation requirements:
- Clear reasoning chain
- Evidence quality assessment
- Confidence levels for each diagnosis
- Knowledge gaps identified
- Risk assessment""",
model_name="gpt-4o",
max_loops=1, max_loops=1,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="worker1.json",
) )
# Create agent list
agents = [chief_medical_officer, virologist, internist, synthesizer]
# Define diagnostic flow
flow = f"""{chief_medical_officer.agent_name} -> {virologist.agent_name} -> {internist.agent_name} -> {synthesizer.agent_name}"""
# Initialize worker 2 # Create the swarm system
worker2 = Agent( diagnosis_system = AgentRearrange(
agent_name="Worker2", name="Medical-nlp-diagnosis-swarm",
system_prompt="Summarizes the transcript generated by Worker1", description="natural language symptions to diagnosis report",
model_name="claude-2", agents=agents,
flow=flow,
max_loops=1, max_loops=1,
dashboard=False, output_type="all",
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="worker2.json",
) )
# Create a list of agents # Example usage
agents = [director, worker1, worker2] if __name__ == "__main__":
# Example patient case
patient_case = """
Patient: 45-year-old female
Presenting symptoms:
- Fever (101.5°F) for 3 days
- Dry cough
- Fatigue
- Mild shortness of breath
Medical history:
- Controlled hypertension
- No recent travel
- Fully vaccinated for COVID-19
- No known sick contacts
"""
# Define the flow pattern # Add timestamp to the patient case
flow = "Director -> Worker1 -> Worker2" case_info = f"Timestamp: {datetime.now()}\nPatient Information: {patient_case}"
# Run the diagnostic process
diagnosis = diagnosis_system.run(case_info)
# Create a folder and file called reports
create_file_in_folder(
"reports", "medical_analysis_agent_rearrange.md", diagnosis
)
# Using AgentRearrange class
agent_system = AgentRearrange(agents=agents, flow=flow)
output = agent_system.run(
"Create a format to express and communicate swarms of llms in a structured manner for youtube"
)
print(output)
``` ```

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