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96 lines
3.0 KiB
96 lines
3.0 KiB
# LLM Council Examples
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This directory contains examples demonstrating the LLM Council pattern, inspired by Andrej Karpathy's llm-council implementation. The LLM Council uses multiple specialized AI agents that:
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1. Each respond independently to queries
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2. Review and rank each other's anonymized responses
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3. Have a Chairman synthesize all responses into a final comprehensive answer
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## Examples
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### Marketing & Business
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- **marketing_strategy_council.py** - Marketing strategy analysis and recommendations
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- **business_strategy_council.py** - Comprehensive business strategy development
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### Finance & Investment
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- **finance_analysis_council.py** - Financial analysis and investment recommendations
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- **etf_stock_analysis_council.py** - ETF and stock analysis with portfolio recommendations
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### Medical & Healthcare
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- **medical_treatment_council.py** - Medical treatment recommendations and care plans
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- **medical_diagnosis_council.py** - Diagnostic analysis based on symptoms
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### Technology & Research
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- **technology_assessment_council.py** - Technology evaluation and implementation strategy
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- **research_analysis_council.py** - Comprehensive research analysis on complex topics
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### Legal
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- **legal_analysis_council.py** - Legal implications and compliance analysis
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## Usage
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Each example follows the same pattern:
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```python
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from swarms.structs.llm_council import LLMCouncil
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# Create the council
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council = LLMCouncil(verbose=True)
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# Run a query
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result = council.run("Your query here")
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# Access results
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print(result["final_response"]) # Chairman's synthesized answer
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print(result["original_responses"]) # Individual member responses
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print(result["evaluations"]) # How members ranked each other
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```
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## Running Examples
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Run any example directly:
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```bash
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python examples/multi_agent/llm_council_examples/marketing_strategy_council.py
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python examples/multi_agent/llm_council_examples/finance_analysis_council.py
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python examples/multi_agent/llm_council_examples/medical_diagnosis_council.py
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```
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## Key Features
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- **Multiple Perspectives**: Each council member (GPT-5.1, Gemini, Claude, Grok) provides unique insights
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- **Peer Review**: Members evaluate and rank each other's responses anonymously
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- **Synthesis**: Chairman combines the best elements from all responses
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- **Transparency**: See both individual responses and evaluation rankings
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## Council Members
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The default council consists of:
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- **GPT-5.1-Councilor**: Analytical and comprehensive
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- **Gemini-3-Pro-Councilor**: Concise and well-processed
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- **Claude-Sonnet-4.5-Councilor**: Thoughtful and balanced
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- **Grok-4-Councilor**: Creative and innovative
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## Customization
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You can create custom council members:
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```python
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from swarms import Agent
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from swarms.structs.llm_council import LLMCouncil, get_gpt_councilor_prompt
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custom_agent = Agent(
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agent_name="Custom-Councilor",
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system_prompt=get_gpt_councilor_prompt(),
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model_name="gpt-4.1",
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max_loops=1,
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)
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council = LLMCouncil(
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council_members=[custom_agent, ...],
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chairman_model="gpt-5.1",
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verbose=True
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)
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```
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