# Multi-Agent Orchestration Methods

Swarms provides a comprehensive suite of orchestration methods for coordinating multiple AI agents in structured conversations and decision-making processes. These methods enable sophisticated multi-agent interactions like debates, panel discussions, negotiations, and more.

## Overview


| Method                   | Orchestration Description                                                                 | Use Case                                   |
|--------------------------|------------------------------------------------------------------------------------------|--------------------------------------------|
| OneOnOneDebate           | Turn-based debates between two agents. Structured debates where two agents alternate turns presenting arguments. | Philosophical discussions, arguments       |
| ExpertPanelDiscussion    | Expert panel discussions with a moderator. Multiple experts discuss topics, guided by a moderator. | Professional discussions, expert opinions  |
| RoundTableDiscussion     | Round table discussions with multiple participants. Open discussions among several agents, each contributing equally. | Group discussions, brainstorming           |
| InterviewSeries          | Structured interviews with follow-up questions. One agent interviews another, with dynamic follow-up questions. | Q&A sessions, information gathering        |
| PeerReviewProcess        | Academic peer review processes. Simulated academic review, where agents critique and provide feedback. | Research review, feedback processes        |
| MediationSession         | Mediation sessions for conflict resolution. Agents participate in sessions to resolve disputes, often with a mediator. | Dispute resolution, negotiations           |
| BrainstormingSession     | Brainstorming sessions for idea generation. Collaborative sessions focused on generating new ideas. | Innovation, problem solving                |
| TrialSimulation          | Trial simulations with legal roles. Agents assume legal roles to simulate a courtroom trial. | Legal proceedings, case analysis           |
| CouncilMeeting           | Council meetings with voting procedures. Decision-making meetings where agents vote on proposals. | Governance, voting processes               |
| MentorshipSession        | Mentorship sessions with feedback loops. One or more agents mentor others, providing feedback and guidance. | Learning, guidance                         |
| NegotiationSession       | Complex negotiations between parties. Multi-party negotiations to reach agreements or resolve conflicts. | Business deals, agreements                 |

## OneOnOneDebate

### Description
Simulates a turn-based debate between two agents for a specified number of loops. Each agent takes turns responding to the other's arguments.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| max_loops | int | Number of conversational turns | No | 1 |
| agents | List[Agent] | Two agents for debate | Yes | None |
| img | str | Optional image input | No | None |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes debate between agents |

### Example

Full example: [Philosophy Discussion Example](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/philosophy_discussion_example.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import OneOnOneDebate

# Create debating agents
agent1 = Agent(name="Philosopher1")
agent2 = Agent(name="Philosopher2")

# Initialize debate
debate = OneOnOneDebate(
    max_loops=3,
    agents=[agent1, agent2]
)

# Run debate
result = debate.run("Is artificial intelligence consciousness possible?")
```

## ExpertPanelDiscussion

### Description
Simulates an expert panel discussion with a moderator guiding the conversation. Multiple experts provide insights on a topic with structured rounds.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| max_rounds | int | Number of discussion rounds | No | 3 |
| agents | List[Agent] | Expert panel participants | Yes | None |
| moderator | Agent | Discussion moderator | Yes | None |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes panel discussion |

### Example

Full example: [Healthcare Panel Discussion](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/healthcare_panel_discussion.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import ExpertPanelDiscussion

# Create expert agents
moderator = Agent(name="Moderator")
expert1 = Agent(name="AI_Expert")
expert2 = Agent(name="Ethics_Expert")
expert3 = Agent(name="Neuroscience_Expert")

# Initialize panel
panel = ExpertPanelDiscussion(
    max_rounds=2,
    agents=[expert1, expert2, expert3],
    moderator=moderator
)

# Run panel discussion
result = panel.run("What are the ethical implications of AGI development?")
```

## RoundTableDiscussion

### Description
Simulates a round table where each participant speaks in order, then the cycle repeats. Facilitated discussion with equal participation.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| max_cycles | int | Number of speaking cycles | No | 2 |
| agents | List[Agent] | Round table participants | Yes | None |
| facilitator | Agent | Discussion facilitator | Yes | None |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes round table discussion |

### Example

Full example: [AI Ethics Debate](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/ai_ethics_debate.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import RoundTableDiscussion

# Create participants
facilitator = Agent(name="Facilitator")
participant1 = Agent(name="Participant1")
participant2 = Agent(name="Participant2")
participant3 = Agent(name="Participant3")

# Initialize round table
roundtable = RoundTableDiscussion(
    max_cycles=2,
    agents=[participant1, participant2, participant3],
    facilitator=facilitator
)

# Run discussion
result = roundtable.run("How can we improve renewable energy adoption?")
```

## InterviewSeries

### Description
Conducts a structured interview with follow-up questions. Systematic Q&A with depth through follow-up questions.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| questions | List[str] | Prepared interview questions | No | Default questions |
| interviewer | Agent | Interviewer agent | Yes | None |
| interviewee | Agent | Interviewee agent | Yes | None |
| follow_up_depth | int | Follow-up questions per main question | No | 2 |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes interview series |

### Example

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import InterviewSeries

# Create agents
interviewer = Agent(name="Interviewer")
interviewee = Agent(name="Expert")

# Prepare questions
questions = [
    "What is your background in AI?",
    "How do you see AI evolving in the next decade?",
    "What are the biggest challenges in AI development?"
]

# Initialize interview
interview = InterviewSeries(
    questions=questions,
    interviewer=interviewer,
    interviewee=interviewee,
    follow_up_depth=2
)

# Run interview
result = interview.run("AI Development and Future Prospects")
```

## PeerReviewProcess

### Description
Simulates academic peer review with multiple reviewers and author responses. Structured feedback and revision process.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| reviewers | List[Agent] | Reviewer agents | Yes | None |
| author | Agent | Author agent | Yes | None |
| review_rounds | int | Number of review rounds | No | 2 |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes peer review process |

### Example

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import PeerReviewProcess

# Create agents
author = Agent(name="Author")
reviewer1 = Agent(name="Reviewer1")
reviewer2 = Agent(name="Reviewer2")

# Initialize peer review
review = PeerReviewProcess(
    reviewers=[reviewer1, reviewer2],
    author=author,
    review_rounds=2
)

# Run review process
result = review.run("New Machine Learning Algorithm for Natural Language Processing")
```

## MediationSession

### Description
Simulates a mediation session to resolve conflicts between parties. Facilitated conflict resolution with structured sessions.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| parties | List[Agent] | Disputing parties | Yes | None |
| mediator | Agent | Mediator agent | Yes | None |
| max_sessions | int | Number of mediation sessions | No | 3 |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes mediation session |

### Example

Full example: [Merger Mediation Session](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/merger_mediation_session.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import MediationSession

# Create agents
mediator = Agent(name="Mediator")
party1 = Agent(name="Party1")
party2 = Agent(name="Party2")

# Initialize mediation
mediation = MediationSession(
    parties=[party1, party2],
    mediator=mediator,
    max_sessions=3
)

# Run mediation
result = mediation.run("Resolve intellectual property dispute")
```

## BrainstormingSession

### Description
Simulates a brainstorming session where participants build on each other's ideas. Creative idea generation with collaborative building.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| participants | List[Agent] | Brainstorming participants | Yes | None |
| facilitator | Agent | Session facilitator | Yes | None |
| idea_rounds | int | Number of idea generation rounds | No | 3 |
| build_on_ideas | bool | Whether to build on previous ideas | No | True |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes brainstorming session |

### Example

Full example: [Pharma Research Brainstorm](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/pharma_research_brainstorm.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import BrainstormingSession

# Create agents
facilitator = Agent(name="Facilitator")
participant1 = Agent(name="Creative1")
participant2 = Agent(name="Creative2")
participant3 = Agent(name="Creative3")

# Initialize brainstorming
brainstorm = BrainstormingSession(
    participants=[participant1, participant2, participant3],
    facilitator=facilitator,
    idea_rounds=3,
    build_on_ideas=True
)

# Run brainstorming
result = brainstorm.run("Innovative solutions for urban transportation")
```

## TrialSimulation

### Description
Simulates a legal trial with structured phases and roles. Complete legal proceeding simulation with all participants.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| prosecution | Agent | Prosecution attorney | Yes | None |
| defense | Agent | Defense attorney | Yes | None |
| judge | Agent | Trial judge | Yes | None |
| witnesses | List[Agent] | Trial witnesses | No | None |
| phases | List[str] | Trial phases | No | Default phases |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes trial simulation |

### Example

Full example: [Medical Malpractice Trial](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/medical_malpractice_trial.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import TrialSimulation

# Create agents
judge = Agent(name="Judge")
prosecutor = Agent(name="Prosecutor")
defense = Agent(name="Defense")
witness1 = Agent(name="Witness1")
witness2 = Agent(name="Witness2")

# Initialize trial
trial = TrialSimulation(
    prosecution=prosecutor,
    defense=defense,
    judge=judge,
    witnesses=[witness1, witness2],
    phases=["opening", "testimony", "cross", "closing"]
)

# Run trial
result = trial.run("Patent infringement case")
```

## CouncilMeeting

### Description
Simulates a council meeting with structured discussion and decision-making. Governance process with voting and consensus building.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| council_members | List[Agent] | Council participants | Yes | None |
| chairperson | Agent | Meeting chairperson | Yes | None |
| voting_rounds | int | Number of voting rounds | No | 1 |
| require_consensus | bool | Whether consensus is required | No | False |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes council meeting |

### Example

Full example: [Investment Council Meeting](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/investment_council_meeting.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import CouncilMeeting

# Create agents
chairperson = Agent(name="Chair")
member1 = Agent(name="Member1")
member2 = Agent(name="Member2")
member3 = Agent(name="Member3")

# Initialize council meeting
council = CouncilMeeting(
    council_members=[member1, member2, member3],
    chairperson=chairperson,
    voting_rounds=2,
    require_consensus=True
)

# Run meeting
result = council.run("Vote on new environmental policy")
```

## MentorshipSession

### Description
Simulates a mentorship session with structured learning and feedback. Guided learning process with progress tracking.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| mentor | Agent | Mentor agent | Yes | None |
| mentee | Agent | Mentee agent | Yes | None |
| session_count | int | Number of sessions | No | 3 |
| include_feedback | bool | Whether to include feedback | No | True |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes mentorship session |

### Example

Full example: [Startup Mentorship Program](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/startup_mentorship_program.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import MentorshipSession

# Create agents
mentor = Agent(name="Mentor")
mentee = Agent(name="Mentee")

# Initialize mentorship
mentorship = MentorshipSession(
    mentor=mentor,
    mentee=mentee,
    session_count=3,
    include_feedback=True
)

# Run session
result = mentorship.run("Career development in AI research")
```

## NegotiationSession

### Description
Simulates a negotiation with multiple parties working toward agreement. Complex multi-party negotiation with concessions.

### Parameters

| Parameter | Type | Description | Required | Default |
|-----------|------|-------------|----------|---------|
| parties | List[Agent] | Negotiating parties | Yes | None |
| mediator | Agent | Negotiation mediator | Yes | None |
| negotiation_rounds | int | Number of rounds | No | 5 |
| include_concessions | bool | Whether to allow concessions | No | True |
| output_type | str | Format for conversation history | No | "str-all-except-first" |

### Run Method

| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| run | task: str | list | Executes negotiation session |

### Example

Full example: [NVIDIA-AMD Executive Negotiation](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/orchestration_examples/nvidia_amd_executive_negotiation.py)

```python
from swarms.agents import Agent
from swarms.structs.multi_agent_debates import NegotiationSession

# Create agents
mediator = Agent(name="Mediator")
party1 = Agent(name="Company1")
party2 = Agent(name="Company2")

# Initialize negotiation
negotiation = NegotiationSession(
    parties=[party1, party2],
    mediator=mediator,
    negotiation_rounds=4,
    include_concessions=True
)

# Run negotiation
result = negotiation.run("Merger terms negotiation")
```

## Conclusion

The multi-agent orchestration methods in Swarms provide powerful frameworks for structuring complex interactions between AI agents. Key benefits include:

1. Structured Communication: Each method provides a clear framework for organizing multi-agent interactions
2. Role-Based Interactions: Agents can take on specific roles with defined responsibilities
3. Flexible Configuration: Customizable parameters for controlling interaction flow
4. Scalable Architecture: Support for various numbers of participants and interaction rounds
5. Comprehensive Coverage: Methods for different use cases from debates to negotiations
6. Professional Output: Consistent formatting and organization of conversation history
7. Easy Integration: Simple API for incorporating into larger applications