You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
107 lines
4.4 KiB
107 lines
4.4 KiB
# 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/`](https://github.com/kyegomez/swarms/tree/master/examples/multi_agent/llm_council_examples) directory.
|
|
|
|
### Marketing & Business
|
|
|
|
- **[marketing_strategy_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/marketing_strategy_council.py)** - Marketing strategy analysis and recommendations
|
|
- **[business_strategy_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/business_strategy_council.py)** - Comprehensive business strategy development
|
|
|
|
### Finance & Investment
|
|
|
|
- **[finance_analysis_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/finance_analysis_council.py)** - Financial analysis and investment recommendations
|
|
- **[etf_stock_analysis_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/etf_stock_analysis_council.py)** - ETF and stock analysis with portfolio recommendations
|
|
|
|
### Medical & Healthcare
|
|
|
|
- **[medical_treatment_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/medical_treatment_council.py)** - Medical treatment recommendations and care plans
|
|
- **[medical_diagnosis_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/medical_diagnosis_council.py)** - Diagnostic analysis based on symptoms
|
|
|
|
### Technology & Research
|
|
|
|
- **[technology_assessment_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/technology_assessment_council.py)** - Technology evaluation and implementation strategy
|
|
- **[research_analysis_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/research_analysis_council.py)** - Comprehensive research analysis on complex topics
|
|
|
|
### Legal
|
|
|
|
- **[legal_analysis_council.py](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/llm_council_examples/legal_analysis_council.py)** - Legal implications and compliance analysis
|
|
|
|
## Basic Usage Pattern
|
|
|
|
All examples follow the same pattern:
|
|
|
|
```python
|
|
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:
|
|
|
|
```bash
|
|
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:
|
|
|
|
```python
|
|
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](../swarms/structs/llm_council.md).
|
|
|