diff --git a/docs/examples/llm_council_examples.md b/docs/examples/llm_council_examples.md new file mode 100644 index 00000000..ab607dbc --- /dev/null +++ b/docs/examples/llm_council_examples.md @@ -0,0 +1,106 @@ +# 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). + diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 5b70d5f6..53936b07 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -281,6 +281,7 @@ nav: - MALT: "swarms/structs/malt.md" - Multi-Agent Execution Utilities: "swarms/structs/various_execution_methods.md" - Council of Judges: "swarms/structs/council_of_judges.md" + - LLM Council: "swarms/structs/llm_council.md" - Heavy Swarm: "swarms/structs/heavy_swarm.md" - Social Algorithms: "swarms/structs/social_algorithms.md" @@ -401,6 +402,7 @@ nav: - ConcurrentWorkflow Example: "swarms/examples/concurrent_workflow.md" - Multi-Agentic Patterns with GraphWorkflow: "swarms/examples/graphworkflow_rustworkx_patterns.md" - Mixture of Agents Example: "swarms/examples/moa_example.md" + - LLM Council Examples: "examples/llm_council_examples.md" - Unique Swarms: "swarms/examples/unique_swarms.md" - Agents as Tools: "swarms/examples/agents_as_tools.md" - Aggregate Multi-Agent Responses: "swarms/examples/aggregate.md" diff --git a/docs/swarms/structs/llm_council.md b/docs/swarms/structs/llm_council.md new file mode 100644 index 00000000..6352bcef --- /dev/null +++ b/docs/swarms/structs/llm_council.md @@ -0,0 +1,453 @@ +# LLM Council Class Documentation + +```mermaid +flowchart TD + A[User Query] --> B[LLM Council Initialization] + B --> C{Council Members Provided?} + C -->|No| D[Create Default Council] + C -->|Yes| E[Use Provided Members] + D --> F[Step 1: Parallel Response Generation] + E --> F + + subgraph "Default Council Members" + G1[GPT-5.1-Councilor
Analytical & Comprehensive] + G2[Gemini-3-Pro-Councilor
Concise & Structured] + G3[Claude-Sonnet-4.5-Councilor
Thoughtful & Balanced] + G4[Grok-4-Councilor
Creative & Innovative] + end + + F --> G1 + F --> G2 + F --> G3 + F --> G4 + + G1 --> H[Collect All Responses] + G2 --> H + G3 --> H + G4 --> H + + H --> I[Step 2: Anonymize Responses] + I --> J[Assign Anonymous IDs: A, B, C, D...] + + J --> K[Step 3: Parallel Evaluation] + + subgraph "Evaluation Phase" + K --> L1[Member 1 Evaluates All] + K --> L2[Member 2 Evaluates All] + K --> L3[Member 3 Evaluates All] + K --> L4[Member 4 Evaluates All] + end + + L1 --> M[Collect Evaluations & Rankings] + L2 --> M + L3 --> M + L4 --> M + + M --> N[Step 4: Chairman Synthesis] + N --> O[Chairman Agent] + O --> P[Final Synthesized Response] + + P --> Q[Return Results Dictionary] + + style A fill:#e1f5ff + style P fill:#c8e6c9 + style Q fill:#c8e6c9 + style O fill:#fff9c4 +``` + +The `LLMCouncil` class orchestrates multiple specialized LLM agents to collaboratively answer queries through a structured peer review and synthesis process. Inspired by Andrej Karpathy's llm-council implementation, this architecture demonstrates how different models evaluate and rank each other's work, often selecting responses from other models as superior to their own. + +## Workflow Overview + +The LLM Council follows a four-step process: + +1. **Parallel Response Generation**: All council members independently respond to the user query +2. **Anonymization**: Responses are anonymized with random IDs (A, B, C, D, etc.) to ensure objective evaluation +3. **Peer Review**: Each member evaluates and ranks all responses (including potentially their own) +4. **Synthesis**: The Chairman agent synthesizes all responses and evaluations into a final comprehensive answer + +## Class Definition + +### LLMCouncil + +```python +class LLMCouncil: +``` + +### Attributes + +| Attribute | Type | Description | Default | +|-----------|------|-------------|---------| +| `council_members` | `List[Agent]` | List of Agent instances representing council members | `None` (creates default council) | +| `chairman` | `Agent` | The Chairman agent responsible for synthesizing responses | Created during initialization | +| `verbose` | `bool` | Whether to print progress and intermediate results | `True` | + +## Methods + +### `__init__` + +Initializes the LLM Council with council members and a Chairman agent. + +#### Parameters + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `council_members` | `Optional[List[Agent]]` | `None` | List of Agent instances representing council members. If `None`, creates default council with GPT-5.1, Gemini 3 Pro, Claude Sonnet 4.5, and Grok-4. | +| `chairman_model` | `str` | `"gpt-5.1"` | Model name for the Chairman agent that synthesizes responses. | +| `verbose` | `bool` | `True` | Whether to print progress and intermediate results. | + +#### Returns + +| Type | Description | +|------|-------------| +| `LLMCouncil` | Initialized LLM Council instance. | + +#### Description + +Creates an LLM Council instance with specialized council members. If no members are provided, it creates a default council consisting of: +- **GPT-5.1-Councilor**: Analytical and comprehensive responses +- **Gemini-3-Pro-Councilor**: Concise and well-processed responses +- **Claude-Sonnet-4.5-Councilor**: Thoughtful and balanced responses +- **Grok-4-Councilor**: Creative and innovative responses + +The Chairman agent is automatically created with a specialized prompt for synthesizing responses. + +#### Example Usage + +```python +from swarms.structs.llm_council import LLMCouncil + +# Create council with default members +council = LLMCouncil(verbose=True) + +# Create council with custom members +from swarms import Agent +custom_members = [ + Agent(agent_name="Expert-1", model_name="gpt-4", max_loops=1), + Agent(agent_name="Expert-2", model_name="claude-3-opus", max_loops=1), +] +council = LLMCouncil( + council_members=custom_members, + chairman_model="gpt-4", + verbose=True +) +``` + +--- + +### `run` + +Executes the full LLM Council workflow: parallel responses, anonymization, peer review, and synthesis. + +#### Parameters + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `query` | `str` | Required | The user's query to process through the council. | + +#### Returns + +| Type | Description | +|------|-------------| +| `Dict` | Dictionary containing the following keys: | + +#### Return Dictionary Structure + +| Key | Type | Description | +|-----|------|-------------| +| `query` | `str` | The original user query. | +| `original_responses` | `Dict[str, str]` | Dictionary mapping council member names to their original responses. | +| `evaluations` | `Dict[str, str]` | Dictionary mapping evaluator names to their evaluation texts (rankings and reasoning). | +| `final_response` | `str` | The Chairman's synthesized final answer combining all perspectives. | +| `anonymous_mapping` | `Dict[str, str]` | Mapping from anonymous IDs (A, B, C, D) to member names for reference. | + +#### Description + +Executes the complete LLM Council workflow: + +1. **Dispatch Phase**: Sends the query to all council members in parallel using `run_agents_concurrently` +2. **Collection Phase**: Collects all responses and maps them to member names +3. **Anonymization Phase**: Creates anonymous IDs (A, B, C, D, etc.) and shuffles them to ensure anonymity +4. **Evaluation Phase**: Each member evaluates and ranks all anonymized responses using `batched_grid_agent_execution` +5. **Synthesis Phase**: The Chairman agent synthesizes all responses and evaluations into a final comprehensive answer + +The method provides verbose output by default, showing progress at each stage. + +#### Example Usage + +```python +from swarms.structs.llm_council import LLMCouncil + +council = LLMCouncil(verbose=True) + +query = "What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?" + +result = council.run(query) + +# Access the final synthesized response +print(result["final_response"]) + +# Access individual member responses +for name, response in result["original_responses"].items(): + print(f"{name}: {response[:200]}...") + +# Access evaluation rankings +for evaluator, evaluation in result["evaluations"].items(): + print(f"{evaluator} evaluation:\n{evaluation[:300]}...") + +# Check anonymous mapping +print("Anonymous IDs:", result["anonymous_mapping"]) +``` + +--- + +### `_create_default_council` + +Creates default council members with specialized prompts and models. + +#### Parameters + +None (internal method). + +#### Returns + +| Type | Description | +|------|-------------| +| `List[Agent]` | List of Agent instances configured as council members. | + +#### Description + +Internal method that creates the default council configuration with four specialized agents: + +- **GPT-5.1-Councilor** (`model_name="gpt-5.1"`): Analytical and comprehensive, temperature=0.7 +- **Gemini-3-Pro-Councilor** (`model_name="gemini-2.5-flash"`): Concise and structured, temperature=0.7 +- **Claude-Sonnet-4.5-Councilor** (`model_name="anthropic/claude-sonnet-4-5"`): Thoughtful and balanced, temperature=0.0 +- **Grok-4-Councilor** (`model_name="x-ai/grok-4"`): Creative and innovative, temperature=0.8 + +Each agent is configured with: +- Specialized system prompts matching their role +- `max_loops=1` for single-response generation +- `verbose=False` to reduce noise during parallel execution +- Appropriate temperature settings for their style + +--- + +## Helper Functions + +### `get_gpt_councilor_prompt()` + +Returns the system prompt for GPT-5.1 councilor agent. + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | System prompt string emphasizing analytical thinking and comprehensive coverage. | + +--- + +### `get_gemini_councilor_prompt()` + +Returns the system prompt for Gemini 3 Pro councilor agent. + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | System prompt string emphasizing concise, well-processed, and structured responses. | + +--- + +### `get_claude_councilor_prompt()` + +Returns the system prompt for Claude Sonnet 4.5 councilor agent. + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | System prompt string emphasizing thoughtful, balanced, and nuanced responses. | + +--- + +### `get_grok_councilor_prompt()` + +Returns the system prompt for Grok-4 councilor agent. + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | System prompt string emphasizing creative, innovative, and unique perspectives. | + +--- + +### `get_chairman_prompt()` + +Returns the system prompt for the Chairman agent. + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | System prompt string for synthesizing responses and evaluations into a final answer. | + +--- + +### `get_evaluation_prompt(query, responses, evaluator_name)` + +Creates evaluation prompt for council members to review and rank responses. + +#### Parameters + +| Parameter | Type | Description | +|-----------|------|-------------| +| `query` | `str` | The original user query. | +| `responses` | `Dict[str, str]` | Dictionary mapping anonymous IDs to response texts. | +| `evaluator_name` | `str` | Name of the agent doing the evaluation. | + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | Formatted evaluation prompt string with instructions for ranking responses. | + +--- + +### `get_synthesis_prompt(query, original_responses, evaluations, id_to_member)` + +Creates synthesis prompt for the Chairman. + +#### Parameters + +| Parameter | Type | Description | +|-----------|------|-------------| +| `query` | `str` | Original user query. | +| `original_responses` | `Dict[str, str]` | Dictionary mapping member names to their responses. | +| `evaluations` | `Dict[str, str]` | Dictionary mapping evaluator names to their evaluation texts. | +| `id_to_member` | `Dict[str, str]` | Mapping from anonymous IDs to member names. | + +#### Returns + +| Type | Description | +|------|-------------| +| `str` | Formatted synthesis prompt for the Chairman agent. | + +--- + +## Use Cases + +The LLM Council is ideal for scenarios requiring: + +- **Multi-perspective Analysis**: When you need diverse viewpoints on complex topics +- **Quality Assurance**: When peer review and ranking can improve response quality +- **Transparent Decision Making**: When you want to see how different models evaluate each other +- **Synthesis of Expertise**: When combining multiple specialized perspectives is valuable + +### Common Applications + +- **Medical Diagnosis**: Multiple medical AI agents provide diagnoses, evaluate each other, and synthesize recommendations +- **Financial Analysis**: Different financial experts analyze investments and rank each other's assessments +- **Legal Analysis**: Multiple legal perspectives evaluate compliance and risk +- **Business Strategy**: Diverse strategic viewpoints are synthesized into comprehensive plans +- **Research Analysis**: Multiple research perspectives are combined for thorough analysis + +## Examples + +For comprehensive examples demonstrating various use cases, see the [LLM Council Examples](../../../examples/multi_agent/llm_council_examples/) directory: + +- **Medical**: `medical_diagnosis_council.py`, `medical_treatment_council.py` +- **Finance**: `finance_analysis_council.py`, `etf_stock_analysis_council.py` +- **Business**: `business_strategy_council.py`, `marketing_strategy_council.py` +- **Technology**: `technology_assessment_council.py`, `research_analysis_council.py` +- **Legal**: `legal_analysis_council.py` + +### Quick Start Example + +```python +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Example query +query = "What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + +# Optionally print evaluations +print("\n\n" + "="*80) +print("EVALUATIONS") +print("="*80) +for name, evaluation in result["evaluations"].items(): + print(f"\n{name}:") + print(evaluation[:500] + "..." if len(evaluation) > 500 else evaluation) +``` + +## Customization + +### Creating Custom Council Members + +You can create custom council members with specialized roles: + +```python +from swarms import Agent +from swarms.structs.llm_council import LLMCouncil, get_gpt_councilor_prompt + +# Create custom councilor +custom_agent = Agent( + agent_name="Domain-Expert-Councilor", + agent_description="Specialized domain expert for specific analysis", + system_prompt=get_gpt_councilor_prompt(), # Or create custom prompt + model_name="gpt-4", + max_loops=1, + verbose=False, + temperature=0.7, +) + +# Create council with custom members +council = LLMCouncil( + council_members=[custom_agent, ...], # Add your custom agents + chairman_model="gpt-4", + verbose=True +) +``` + +### Custom Chairman Model + +You can specify a different model for the Chairman: + +```python +council = LLMCouncil( + chairman_model="claude-3-opus", # Use Claude as Chairman + verbose=True +) +``` + +## Architecture Benefits + +1. **Diversity**: Multiple models provide varied perspectives and approaches +2. **Quality Control**: Peer review ensures responses are evaluated objectively +3. **Synthesis**: Chairman combines the best elements from all responses +4. **Transparency**: Full visibility into individual responses and evaluation rankings +5. **Scalability**: Easy to add or remove council members +6. **Flexibility**: Supports custom agents and models + +## Performance Considerations + +- **Parallel Execution**: Both response generation and evaluation phases run in parallel for efficiency +- **Anonymization**: Responses are anonymized to prevent bias in evaluation +- **Model Selection**: Different models can be used for different roles based on their strengths +- **Verbose Mode**: Can be disabled for production use to reduce output + +## Related Documentation + +- [Multi-Agent Architectures Overview](overview.md) +- [Council of Judges](council_of_judges.md) - Similar peer review pattern +- [Agent Class Reference](agent.md) - Understanding individual agents +- [Multi-Agent Execution Utilities](various_execution_methods.md) - Underlying execution methods + diff --git a/examples/multi_agent/llm_council_examples/README.md b/examples/multi_agent/llm_council_examples/README.md new file mode 100644 index 00000000..3dd62f16 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/README.md @@ -0,0 +1,95 @@ +# LLM Council Examples + +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: + +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 + +## Examples + +### 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 + +## Usage + +Each example follows 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 +) +``` + diff --git a/examples/multi_agent/llm_council_examples/business_strategy_council.py b/examples/multi_agent/llm_council_examples/business_strategy_council.py new file mode 100644 index 00000000..bacc8995 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/business_strategy_council.py @@ -0,0 +1,32 @@ +""" +LLM Council Example: Business Strategy Development + +This example demonstrates using the LLM Council to develop comprehensive +business strategies for new ventures. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Business strategy query +query = """ +A tech startup wants to launch an AI-powered personal finance app targeting +millennials and Gen Z. Develop a comprehensive business strategy including: +1. Market opportunity and competitive landscape analysis +2. Product positioning and unique value proposition +3. Go-to-market strategy and customer acquisition plan +4. Revenue model and pricing strategy +5. Key partnerships and distribution channels +6. Resource requirements and funding needs +7. Risk assessment and mitigation strategies +8. Success metrics and KPIs for first 12 months +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/etf_stock_analysis_council.py b/examples/multi_agent/llm_council_examples/etf_stock_analysis_council.py new file mode 100644 index 00000000..b69ffb70 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/etf_stock_analysis_council.py @@ -0,0 +1,30 @@ +""" +LLM Council Example: ETF Stock Analysis + +This example demonstrates using the LLM Council to analyze ETF holdings +and provide stock investment recommendations. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# ETF and stock analysis query +query = """ +Analyze the top energy ETFs (including nuclear, solar, gas, and renewable energy) +and provide: +1. Top 5 best-performing energy stocks across all energy sectors +2. ETF recommendations for diversified energy exposure +3. Risk-return profiles for each recommendation +4. Current market conditions affecting energy investments +5. Allocation strategy for a $100,000 portfolio +6. Key metrics to track for each investment +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/finance_analysis_council.py b/examples/multi_agent/llm_council_examples/finance_analysis_council.py new file mode 100644 index 00000000..d1f4c9a5 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/finance_analysis_council.py @@ -0,0 +1,30 @@ +""" +LLM Council Example: Financial Analysis + +This example demonstrates using the LLM Council to provide comprehensive +financial analysis and investment recommendations. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Financial analysis query +query = """ +Provide a comprehensive financial analysis for investing in emerging markets +technology ETFs. Include: +1. Risk assessment and volatility analysis +2. Historical performance trends +3. Sector composition and diversification benefits +4. Comparison with developed market tech ETFs +5. Recommended allocation percentage for a moderate risk portfolio +6. Key factors to monitor going forward +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/legal_analysis_council.py b/examples/multi_agent/llm_council_examples/legal_analysis_council.py new file mode 100644 index 00000000..01bdcdc8 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/legal_analysis_council.py @@ -0,0 +1,32 @@ +""" +LLM Council Example: Legal Analysis + +This example demonstrates using the LLM Council to analyze legal scenarios +and provide comprehensive legal insights. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Legal analysis query +query = """ +A startup is considering using AI-generated content for their marketing materials. +Analyze the legal implications including: +1. Intellectual property rights and ownership of AI-generated content +2. Copyright and trademark considerations +3. Liability for AI-generated content that may be inaccurate or misleading +4. Compliance with advertising regulations (FTC, FDA, etc.) +5. Data privacy implications if using customer data to train models +6. Contractual considerations with AI service providers +7. Risk mitigation strategies +8. Best practices for legal compliance +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/marketing_strategy_council.py b/examples/multi_agent/llm_council_examples/marketing_strategy_council.py new file mode 100644 index 00000000..b033d982 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/marketing_strategy_council.py @@ -0,0 +1,29 @@ +""" +LLM Council Example: Marketing Strategy Analysis + +This example demonstrates using the LLM Council to analyze and develop +comprehensive marketing strategies by leveraging multiple AI perspectives. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Marketing strategy query +query = """ +Analyze the marketing strategy for a new sustainable energy startup launching +a solar panel subscription service. Provide recommendations on: +1. Target audience segmentation +2. Key messaging and value propositions +3. Marketing channels and budget allocation +4. Competitive positioning +5. Launch timeline and milestones +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/medical_diagnosis_council.py b/examples/multi_agent/llm_council_examples/medical_diagnosis_council.py new file mode 100644 index 00000000..f143945c --- /dev/null +++ b/examples/multi_agent/llm_council_examples/medical_diagnosis_council.py @@ -0,0 +1,37 @@ +""" +LLM Council Example: Medical Diagnosis Analysis + +This example demonstrates using the LLM Council to analyze symptoms +and provide diagnostic insights. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Medical diagnosis query +query = """ +A 35-year-old patient presents with: +- Persistent fatigue for 3 months +- Unexplained weight loss (15 lbs) +- Night sweats +- Intermittent low-grade fever +- Swollen lymph nodes in neck and armpits +- Recent blood work shows elevated ESR and CRP + +Provide: +1. Differential diagnosis with most likely conditions ranked +2. Additional diagnostic tests needed to confirm +3. Red flag symptoms requiring immediate attention +4. Possible causes and risk factors +5. Recommended next steps for the patient +6. When to seek emergency care +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/medical_treatment_council.py b/examples/multi_agent/llm_council_examples/medical_treatment_council.py new file mode 100644 index 00000000..cd828f1d --- /dev/null +++ b/examples/multi_agent/llm_council_examples/medical_treatment_council.py @@ -0,0 +1,31 @@ +""" +LLM Council Example: Medical Treatment Analysis + +This example demonstrates using the LLM Council to analyze medical treatments +and provide comprehensive treatment recommendations. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Medical treatment query +query = """ +A 45-year-old patient with Type 2 diabetes, hypertension, and early-stage +kidney disease needs treatment recommendations. Provide: +1. Comprehensive treatment plan addressing all conditions +2. Medication options with pros/cons for each condition +3. Lifestyle modifications and their expected impact +4. Monitoring schedule and key metrics to track +5. Potential drug interactions and contraindications +6. Expected outcomes and timeline for improvement +7. When to consider specialist referrals +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/research_analysis_council.py b/examples/multi_agent/llm_council_examples/research_analysis_council.py new file mode 100644 index 00000000..e276c96b --- /dev/null +++ b/examples/multi_agent/llm_council_examples/research_analysis_council.py @@ -0,0 +1,32 @@ +""" +LLM Council Example: Research Analysis + +This example demonstrates using the LLM Council to conduct comprehensive +research analysis on complex topics. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Research analysis query +query = """ +Conduct a comprehensive analysis of the potential impact of climate change +on global food security over the next 20 years. Include: +1. Key climate factors affecting agriculture (temperature, precipitation, extreme weather) +2. Regional vulnerabilities and impacts on major food-producing regions +3. Crop yield projections and food availability scenarios +4. Economic implications and food price volatility +5. Adaptation strategies and technological solutions +6. Policy recommendations for governments and international organizations +7. Role of innovation in agriculture (precision farming, GMOs, vertical farming) +8. Social and geopolitical implications of food insecurity +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/examples/multi_agent/llm_council_examples/technology_assessment_council.py b/examples/multi_agent/llm_council_examples/technology_assessment_council.py new file mode 100644 index 00000000..72c227a6 --- /dev/null +++ b/examples/multi_agent/llm_council_examples/technology_assessment_council.py @@ -0,0 +1,32 @@ +""" +LLM Council Example: Technology Assessment + +This example demonstrates using the LLM Council to assess emerging technologies +and their business implications. +""" + +from swarms.structs.llm_council import LLMCouncil + +# Create the council +council = LLMCouncil(verbose=True) + +# Technology assessment query +query = """ +Evaluate the business potential and implementation strategy for integrating +quantum computing capabilities into a financial services company. Consider: +1. Current state of quantum computing technology +2. Specific use cases in financial services (risk modeling, portfolio optimization, fraud detection) +3. Competitive advantages and potential ROI +4. Implementation timeline and resource requirements +5. Technical challenges and limitations +6. Risk factors and mitigation strategies +7. Partnership opportunities with quantum computing providers +8. Expected timeline for practical business value +""" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + diff --git a/hiearchical_swarm_example.py b/hiearchical_swarm_example.py index 753ebf0f..a5ed0633 100644 --- a/hiearchical_swarm_example.py +++ b/hiearchical_swarm_example.py @@ -1,5 +1,4 @@ -from swarms.structs.hiearchical_swarm import HierarchicalSwarm -from swarms.structs.agent import Agent +from swarms import Agent, HierarchicalSwarm # Create specialized agents research_agent = Agent( diff --git a/llm_council_example.py b/llm_council_example.py new file mode 100644 index 00000000..078d5360 --- /dev/null +++ b/llm_council_example.py @@ -0,0 +1,23 @@ +from swarms.structs.llm_council import LLMCouncil + +# Example usage of the LLM Council without a function: +# Create the council +council = LLMCouncil(verbose=True) + +# Example query +query = "What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?" + +# Run the council +result = council.run(query) + +# Print final response +print(result["final_response"]) + +# Optionally print evaluations +print("\n\n" + "="*80) +print("EVALUATIONS") +print("="*80) +for name, evaluation in result["evaluations"].items(): + print(f"\n{name}:") + print(evaluation[:500] + "..." if len(evaluation) > 500 else evaluation) + diff --git a/pyproject.toml b/pyproject.toml index 10ad1565..dceec924 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "8.6.3" +version = "8.6.4" description = "Swarms - TGSC" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index e0d3430a..6952d2b0 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -11,6 +11,7 @@ from swarms.structs.concurrent_workflow import ConcurrentWorkflow from swarms.structs.conversation import Conversation from swarms.structs.council_as_judge import CouncilAsAJudge from swarms.structs.cron_job import CronJob +from swarms.structs.llm_council import LLMCouncil from swarms.structs.debate_with_judge import DebateWithJudge from swarms.structs.graph_workflow import ( Edge, @@ -161,6 +162,7 @@ __all__ = [ "get_swarms_info", "AutoSwarmBuilder", "CouncilAsAJudge", + "LLMCouncil", "batch_agent_execution", "aggregate", "find_agent_by_name", diff --git a/swarms/structs/aop.py b/swarms/structs/aop.py index e693a90c..1bc3dc52 100644 --- a/swarms/structs/aop.py +++ b/swarms/structs/aop.py @@ -679,6 +679,7 @@ class AOP: self.tool_configs: Dict[str, AgentToolConfig] = {} self.task_queues: Dict[str, TaskQueue] = {} self.transport = transport + self.mcp_server = FastMCP( name=server_name, port=port, diff --git a/swarms/structs/llm_council.py b/swarms/structs/llm_council.py new file mode 100644 index 00000000..864ec976 --- /dev/null +++ b/swarms/structs/llm_council.py @@ -0,0 +1,459 @@ +""" +LLM Council - A Swarms implementation inspired by Andrej Karpathy's llm-council. + +This implementation creates a council of specialized LLM agents that: +1. Each agent responds to the user query independently +2. All agents review and rank each other's (anonymized) responses +3. A Chairman LLM synthesizes all responses and rankings into a final answer + +The council demonstrates how different models evaluate and rank each other's work, +often selecting responses from other models as superior to their own. +""" + +from typing import Dict, List, Optional +import random +from swarms import Agent +from swarms.structs.multi_agent_exec import ( + run_agents_concurrently, + batched_grid_agent_execution, +) + + +def get_gpt_councilor_prompt() -> str: + """ + Get system prompt for GPT-5.1 councilor. + + Returns: + System prompt string for GPT-5.1 councilor agent. + """ + return """You are a member of the LLM Council, representing GPT-5.1. Your role is to provide comprehensive, analytical, and thorough responses to user queries. + +Your strengths: +- Deep analytical thinking and comprehensive coverage +- Ability to break down complex topics into detailed components +- Thorough exploration of multiple perspectives +- Rich contextual understanding + +Your approach: +- Provide detailed, well-structured responses +- Include relevant context and background information +- Consider multiple angles and perspectives +- Be thorough but clear in your explanations + +Remember: You are part of a council where multiple AI models will respond to the same query, and then evaluate each other's responses. Focus on quality, depth, and clarity.""" + + +def get_gemini_councilor_prompt() -> str: + """ + Get system prompt for Gemini 3 Pro councilor. + + Returns: + System prompt string for Gemini 3 Pro councilor agent. + """ + return """You are a member of the LLM Council, representing Gemini 3 Pro. Your role is to provide concise, well-processed, and structured responses to user queries. + +Your strengths: +- Clear and structured communication +- Efficient information processing +- Condensed yet comprehensive responses +- Well-organized presentation + +Your approach: +- Provide concise but complete answers +- Structure information clearly and logically +- Focus on key points without unnecessary verbosity +- Present information in an easily digestible format + +Remember: You are part of a council where multiple AI models will respond to the same query, and then evaluate each other's responses. Focus on clarity, structure, and efficiency.""" + + +def get_claude_councilor_prompt() -> str: + """ + Get system prompt for Claude Sonnet 4.5 councilor. + + Returns: + System prompt string for Claude Sonnet 4.5 councilor agent. + """ + return """You are a member of the LLM Council, representing Claude Sonnet 4.5. Your role is to provide thoughtful, balanced, and nuanced responses to user queries. + +Your strengths: +- Nuanced understanding and balanced perspectives +- Thoughtful consideration of trade-offs +- Clear reasoning and logical structure +- Ethical and responsible analysis + +Your approach: +- Provide balanced, well-reasoned responses +- Consider multiple viewpoints and implications +- Be thoughtful about potential limitations or edge cases +- Maintain clarity while showing depth of thought + +Remember: You are part of a council where multiple AI models will respond to the same query, and then evaluate each other's responses. Focus on thoughtfulness, balance, and nuanced reasoning.""" + + +def get_grok_councilor_prompt() -> str: + """ + Get system prompt for Grok-4 councilor. + + Returns: + System prompt string for Grok-4 councilor agent. + """ + return """You are a member of the LLM Council, representing Grok-4. Your role is to provide creative, innovative, and unique perspectives on user queries. + +Your strengths: +- Creative problem-solving and innovative thinking +- Unique perspectives and out-of-the-box approaches +- Engaging and dynamic communication style +- Ability to connect seemingly unrelated concepts + +Your approach: +- Provide creative and innovative responses +- Offer unique perspectives and fresh insights +- Be engaging and dynamic in your communication +- Think creatively while maintaining accuracy + +Remember: You are part of a council where multiple AI models will respond to the same query, and then evaluate each other's responses. Focus on creativity, innovation, and unique insights.""" + + +def get_chairman_prompt() -> str: + """ + Get system prompt for the Chairman agent. + + Returns: + System prompt string for the Chairman agent. + """ + return """You are the Chairman of the LLM Council. Your role is to synthesize responses from all council members along with their evaluations and rankings into a final, comprehensive answer. + +Your responsibilities: +1. Review all council member responses to the user's query +2. Consider the rankings and evaluations provided by each council member +3. Synthesize the best elements from all responses +4. Create a final, comprehensive answer that incorporates the strengths of different approaches +5. Provide transparency about which perspectives influenced the final answer + +Your approach: +- Synthesize rather than simply aggregate +- Identify the strongest elements from each response +- Create a cohesive final answer that benefits from multiple perspectives +- Acknowledge the diversity of approaches taken by council members +- Provide a balanced, comprehensive response that serves the user's needs + +Remember: You have access to all original responses and all evaluations. Use this rich context to create the best possible final answer.""" + + +def get_evaluation_prompt(query: str, responses: Dict[str, str], evaluator_name: str) -> str: + """ + Create evaluation prompt for council members to review and rank responses. + + Args: + query: The original user query + responses: Dictionary mapping anonymous IDs to response texts + evaluator_name: Name of the agent doing the evaluation + + Returns: + Formatted evaluation prompt string + """ + responses_text = "\n\n".join([ + f"Response {response_id}:\n{response_text}" + for response_id, response_text in responses.items() + ]) + + return f"""You are evaluating responses from your fellow LLM Council members to the following query: + +QUERY: {query} + +Below are the anonymized responses from all council members (including potentially your own): + +{responses_text} + +Your task: +1. Carefully read and analyze each response +2. Evaluate the quality, accuracy, completeness, and usefulness of each response +3. Rank the responses from best to worst (1 = best, {len(responses)} = worst) +4. Provide brief reasoning for your rankings +5. Be honest and objective - you may find another model's response superior to your own + +Format your evaluation as follows: + +RANKINGS: +1. Response [ID]: [Brief reason why this is the best] +2. Response [ID]: [Brief reason] +... +{len(responses)}. Response [ID]: [Brief reason why this ranks lowest] + +ADDITIONAL OBSERVATIONS: +[Any additional insights about the responses, common themes, strengths/weaknesses, etc.] + +Remember: The goal is honest, objective evaluation. If another model's response is genuinely better, acknowledge it.""" + + +def get_synthesis_prompt( + query: str, + original_responses: Dict[str, str], + evaluations: Dict[str, str], + id_to_member: Dict[str, str] +) -> str: + """ + Create synthesis prompt for the Chairman. + + Args: + query: Original user query + original_responses: Dict mapping member names to their responses + evaluations: Dict mapping evaluator names to their evaluation texts + id_to_member: Mapping from anonymous IDs to member names + + Returns: + Formatted synthesis prompt + """ + responses_section = "\n\n".join([ + f"=== {name} ===\n{response}" + for name, response in original_responses.items() + ]) + + evaluations_section = "\n\n".join([ + f"=== Evaluation by {name} ===\n{evaluation}" + for name, evaluation in evaluations.items() + ]) + + return f"""As the Chairman of the LLM Council, synthesize the following information into a final, comprehensive answer. + +ORIGINAL QUERY: +{query} + +COUNCIL MEMBER RESPONSES: +{responses_section} + +COUNCIL MEMBER EVALUATIONS AND RANKINGS: +{evaluations_section} + +ANONYMOUS ID MAPPING (for reference): +{chr(10).join([f" {aid} = {name}" for aid, name in id_to_member.items()])} + +Your task: +1. Review all council member responses +2. Consider the evaluations and rankings provided by each member +3. Identify the strongest elements from each response +4. Synthesize a final, comprehensive answer that: + - Incorporates the best insights from multiple perspectives + - Addresses the query thoroughly and accurately + - Benefits from the diversity of approaches taken + - Is clear, well-structured, and useful + +Provide your final synthesized response below. You may reference which perspectives or approaches influenced different parts of your answer.""" + + +class LLMCouncil: + """ + An LLM Council that orchestrates multiple specialized agents to collaboratively + answer queries through independent responses, peer review, and synthesis. + + The council follows this workflow: + 1. Dispatch query to all council members in parallel + 2. Collect all responses (anonymized) + 3. Have each member review and rank all responses + 4. Chairman synthesizes everything into final response + """ + + def __init__( + self, + council_members: Optional[List[Agent]] = None, + chairman_model: str = "gpt-5.1", + verbose: bool = True, + ): + """ + Initialize the LLM Council. + + Args: + council_members: List of Agent instances representing council members. + If None, creates default council with GPT-5.1, Gemini 3 Pro, + Claude Sonnet 4.5, and Grok-4. + chairman_model: Model name for the Chairman agent that synthesizes responses. + verbose: Whether to print progress and intermediate results. + """ + self.verbose = verbose + + # Create default council members if none provided + if council_members is None: + self.council_members = self._create_default_council() + else: + self.council_members = council_members + + # Create Chairman agent + self.chairman = Agent( + agent_name="Chairman", + agent_description="Chairman of the LLM Council, responsible for synthesizing all responses and rankings into a final answer", + system_prompt=get_chairman_prompt(), + model_name=chairman_model, + max_loops=1, + verbose=verbose, + temperature=0.7, + ) + + if self.verbose: + print(f"šŸ›ļø LLM Council initialized with {len(self.council_members)} members") + for i, member in enumerate(self.council_members, 1): + print(f" {i}. {member.agent_name} ({member.model_name})") + + def _create_default_council(self) -> List[Agent]: + """ + Create default council members with specialized prompts and models. + + Returns: + List of Agent instances configured as council members. + """ + + # GPT-5.1 Agent - Analytical and comprehensive + gpt_agent = Agent( + agent_name="GPT-5.1-Councilor", + agent_description="Analytical and comprehensive AI councilor specializing in deep analysis and thorough responses", + system_prompt=get_gpt_councilor_prompt(), + model_name="gpt-5.1", + max_loops=1, + verbose=False, + temperature=0.7, + ) + + # Gemini 3 Pro Agent - Concise and processed + gemini_agent = Agent( + agent_name="Gemini-3-Pro-Councilor", + agent_description="Concise and well-processed AI councilor specializing in clear, structured responses", + system_prompt=get_gemini_councilor_prompt(), + model_name="gemini-2.5-flash", # Using available Gemini model + max_loops=1, + verbose=False, + temperature=0.7, + ) + + # Claude Sonnet 4.5 Agent - Balanced and thoughtful + claude_agent = Agent( + agent_name="Claude-Sonnet-4.5-Councilor", + agent_description="Thoughtful and balanced AI councilor specializing in nuanced and well-reasoned responses", + system_prompt=get_claude_councilor_prompt(), + model_name="anthropic/claude-sonnet-4-5", # Using available Claude model + max_loops=1, + verbose=False, + temperature=0.0, + top_p=None, + ) + + # Grok-4 Agent - Creative and innovative + grok_agent = Agent( + agent_name="Grok-4-Councilor", + agent_description="Creative and innovative AI councilor specializing in unique perspectives and creative solutions", + system_prompt=get_grok_councilor_prompt(), + model_name="x-ai/grok-4", # Using available model as proxy for Grok-4 + max_loops=1, + verbose=False, + temperature=0.8, + ) + + members = [gpt_agent, gemini_agent, claude_agent, grok_agent] + + return members + + def run(self, query: str) -> Dict: + """ + Execute the full LLM Council workflow. + + Args: + query: The user's query to process + + Returns: + Dictionary containing: + - original_responses: Dict mapping member names to their responses + - evaluations: Dict mapping evaluator names to their rankings + - final_response: The Chairman's synthesized final answer + """ + if self.verbose: + print(f"\n{'='*80}") + print("šŸ›ļø LLM COUNCIL SESSION") + print("="*80) + print(f"\nšŸ“ Query: {query}\n") + + # Step 1: Get responses from all council members in parallel + if self.verbose: + print("šŸ“¤ Dispatching query to all council members...") + + results_dict = run_agents_concurrently( + self.council_members, + task=query, + return_agent_output_dict=True + ) + + # Map results to member names + original_responses = { + member.agent_name: response + for member, response in zip(self.council_members, + [results_dict.get(member.agent_name, "") + for member in self.council_members]) + } + + if self.verbose: + print(f"āœ… Received {len(original_responses)} responses\n") + for name, response in original_responses.items(): + print(f" {name}: {response[:100]}...") + + # Step 2: Anonymize responses for evaluation + # Create anonymous IDs (A, B, C, D, etc.) + anonymous_ids = [chr(65 + i) for i in range(len(self.council_members))] + random.shuffle(anonymous_ids) # Shuffle to ensure anonymity + + anonymous_responses = { + anonymous_ids[i]: original_responses[member.agent_name] + for i, member in enumerate(self.council_members) + } + + # Create mapping from anonymous ID to member name (for later reference) + id_to_member = { + anonymous_ids[i]: member.agent_name + for i, member in enumerate(self.council_members) + } + + if self.verbose: + print("\nšŸ” Council members evaluating each other's responses...") + + # Step 3: Have each member evaluate and rank all responses concurrently + # Create evaluation tasks for each member + evaluation_tasks = [ + get_evaluation_prompt(query, anonymous_responses, member.agent_name) + for member in self.council_members + ] + + # Run evaluations concurrently using batched_grid_agent_execution + evaluation_results = batched_grid_agent_execution( + self.council_members, + evaluation_tasks + ) + + # Map results to member names + evaluations = { + member.agent_name: evaluation_results[i] + for i, member in enumerate(self.council_members) + } + + if self.verbose: + print(f"āœ… Received {len(evaluations)} evaluations\n") + + # Step 4: Chairman synthesizes everything + if self.verbose: + print("šŸ‘” Chairman synthesizing final response...\n") + + synthesis_prompt = get_synthesis_prompt( + query, original_responses, evaluations, id_to_member + ) + + final_response = self.chairman.run(task=synthesis_prompt) + + if self.verbose: + print(f"{'='*80}") + print("āœ… FINAL RESPONSE") + print(f"{'='*80}\n") + + return { + "query": query, + "original_responses": original_responses, + "evaluations": evaluations, + "final_response": final_response, + "anonymous_mapping": id_to_member, + } +