swarms/docs/examples/llm_council_examples.md

LLM Council Examples

This page provides examples demonstrating the LLM Council pattern, inspired by Andrej Karpathy's llm-council implementation. The LLM Council uses multiple specialized AI agents that:

1. Each respond independently to queries
2. Review and rank each other's anonymized responses
3. Have a Chairman synthesize all responses into a final comprehensive answer

Example Files

All LLM Council examples are located in the examples/multi_agent/llm_council_examples/ directory.

Marketing & Business

• marketing_strategy_council.py - Marketing strategy analysis and recommendations
• business_strategy_council.py - Comprehensive business strategy development

Finance & Investment

• finance_analysis_council.py - Financial analysis and investment recommendations
• etf_stock_analysis_council.py - ETF and stock analysis with portfolio recommendations

Medical & Healthcare

• medical_treatment_council.py - Medical treatment recommendations and care plans
• medical_diagnosis_council.py - Diagnostic analysis based on symptoms

Technology & Research

• technology_assessment_council.py - Technology evaluation and implementation strategy
• research_analysis_council.py - Comprehensive research analysis on complex topics

Legal

• legal_analysis_council.py - Legal implications and compliance analysis

Basic Usage Pattern

All examples follow the same pattern:

from swarms.structs.llm_council import LLMCouncil

# Create the council
council = LLMCouncil(verbose=True)

# Run a query
result = council.run("Your query here")

# Access results
print(result["final_response"])  # Chairman's synthesized answer
print(result["original_responses"])  # Individual member responses
print(result["evaluations"])  # How members ranked each other

Running Examples

Run any example directly:

python examples/multi_agent/llm_council_examples/marketing_strategy_council.py
python examples/multi_agent/llm_council_examples/finance_analysis_council.py
python examples/multi_agent/llm_council_examples/medical_diagnosis_council.py

Key Features

• Multiple Perspectives: Each council member (GPT-5.1, Gemini, Claude, Grok) provides unique insights
• Peer Review: Members evaluate and rank each other's responses anonymously
• Synthesis: Chairman combines the best elements from all responses
• Transparency: See both individual responses and evaluation rankings

Council Members

The default council consists of:

• GPT-5.1-Councilor: Analytical and comprehensive
• Gemini-3-Pro-Councilor: Concise and well-processed
• Claude-Sonnet-4.5-Councilor: Thoughtful and balanced
• Grok-4-Councilor: Creative and innovative

Customization

You can create custom council members:

from swarms import Agent
from swarms.structs.llm_council import LLMCouncil, get_gpt_councilor_prompt

custom_agent = Agent(
    agent_name="Custom-Councilor",
    system_prompt=get_gpt_councilor_prompt(),
    model_name="gpt-4.1",
    max_loops=1,
)

council = LLMCouncil(
    council_members=[custom_agent, ...],
    chairman_model="gpt-5.1",
    verbose=True
)

Documentation

For complete API reference and detailed documentation, see the LLM Council Reference Documentation.