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

Feature	Description
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:

Council Member	Description
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.