[Improve LLMCouncil] [Improved docs]

docs/swarms/structs/llm_council.md

@@ -2,61 +2,35 @@
 
 ```mermaid
 flowchart TD
-    A[User Query] --> B[LLM Council Initialization]
+    A[User Query] --> B[Council Members]
-    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"
+    subgraph "Council Members"
-        G1[GPT-5.1-Councilor<br/>Analytical & Comprehensive]
+        C1[GPT-5.1-Councilor]
-        G2[Gemini-3-Pro-Councilor<br/>Concise & Structured]
+        C2[Gemini-3-Pro-Councilor]
-        G3[Claude-Sonnet-4.5-Councilor<br/>Thoughtful & Balanced]
+        C3[Claude-Sonnet-4.5-Councilor]
-        G4[Grok-4-Councilor<br/>Creative & Innovative]
+        C4[Grok-4-Councilor]
     end
     
-    F --> G1
+    B --> C1
-    F --> G2
+    B --> C2
-    F --> G3
+    B --> C3
-    F --> G4
+    B --> C4
     
-    G1 --> H[Collect All Responses]
+    C1 --> D[Responses]
-    G2 --> H
+    C2 --> D
-    G3 --> H
+    C3 --> D
-    G4 --> H
+    C4 --> D
     
-    H --> I[Step 2: Anonymize Responses]
+    D --> E[Anonymize & Evaluate]
-    I --> J[Assign Anonymous IDs: A, B, C, D...]
+    E --> F[Chairman Synthesis]
-    
+    F --> G[Final Response]
-    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.
 
+The class automatically tracks all agent messages in a `Conversation` object and formats output using `history_output_formatter`, providing flexible output formats including dictionaries, lists, strings, JSON, YAML, and more.
+
 ## Workflow Overview
 
 The LLM Council follows a four-step process:
@@ -80,6 +54,8 @@ class LLMCouncil:
 |-----------|------|-------------|---------|
 | `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 |
+| `conversation` | `Conversation` | Conversation object tracking all messages throughout the workflow | Created during initialization |
+| `output_type` | `HistoryOutputType` | Format for the output (e.g., "dict", "list", "string", "json", "yaml") | `"dict"` |
 | `verbose` | `bool` | Whether to print progress and intermediate results | `True` |
 
 ## Methods
@@ -92,9 +68,13 @@ Initializes the LLM Council with council members and a Chairman agent.
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
+| `id` | `str` | `swarm_id()` | Unique identifier for the council instance. |
+| `name` | `str` | `"LLM Council"` | Name of the council instance. |
+| `description` | `str` | `"A collaborative council..."` | Description of the council's purpose. |
 | `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. |
+| `output_type` | `HistoryOutputType` | `"dict"` | Format for the output. Options: "list", "dict", "string", "final", "json", "yaml", "xml", "dict-all-except-first", "str-all-except-first", "dict-final", "list-final". |
 
 #### Returns
 
@@ -105,12 +85,13 @@ Initializes the LLM Council with council members and a Chairman agent.
 #### 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.
+The Chairman agent is automatically created with a specialized prompt for synthesizing responses. A `Conversation` object is also initialized to track all messages throughout the workflow, including user queries, council member responses, evaluations, and the final synthesis.
 
 #### Example Usage
 
@@ -120,7 +101,7 @@ from swarms.structs.llm_council import LLMCouncil
 # Create council with default members
 council = LLMCouncil(verbose=True)
 
-# Create council with custom members
+# Create council with custom members and output format
 from swarms import Agent
 custom_members = [
     Agent(agent_name="Expert-1", model_name="gpt-4", max_loops=1),
@@ -129,7 +110,8 @@ custom_members = [
 council = LLMCouncil(
     council_members=custom_members,
     chairman_model="gpt-4",
-    verbose=True
+    verbose=True,
+    output_type="json"  # Output as JSON string
 )
 ```
 
@@ -137,7 +119,7 @@ council = LLMCouncil(
 
 ### `run`
 
-Executes the full LLM Council workflow: parallel responses, anonymization, peer review, and synthesis.
+Executes the full LLM Council workflow: parallel responses, anonymization, peer review, and synthesis. All messages are tracked in the conversation object and formatted according to the `output_type` setting.
 
 #### Parameters
 
@@ -149,54 +131,79 @@ Executes the full LLM Council workflow: parallel responses, anonymization, peer
 
 | Type | Description |
 |------|-------------|
-| `Dict` | Dictionary containing the following keys: |
+| `Union[List, Dict, str]` | Formatted output based on `output_type`. The output contains the conversation history with all messages tracked throughout the workflow. |
 
-#### Return Dictionary Structure
+#### Output Format
 
-| Key | Type | Description |
+The return value depends on the `output_type` parameter set during initialization:
-|-----|------|-------------|
+
-| `query` | `str` | The original user query. |
+- **`"dict"`** (default): Returns conversation as a dictionary/list of message dictionaries
-| `original_responses` | `Dict[str, str]` | Dictionary mapping council member names to their original responses. |
+- **`"list"`**: Returns conversation as a list of formatted strings (`"role: content"`)
-| `evaluations` | `Dict[str, str]` | Dictionary mapping evaluator names to their evaluation texts (rankings and reasoning). |
+- **`"string"`** or **`"str"`**: Returns conversation as a formatted string
-| `final_response` | `str` | The Chairman's synthesized final answer combining all perspectives. |
+- **`"final"`** or **`"last"`**: Returns only the content of the final message (Chairman's response)
-| `anonymous_mapping` | `Dict[str, str]` | Mapping from anonymous IDs (A, B, C, D) to member names for reference. |
+- **`"json"`**: Returns conversation as a JSON string
+- **`"yaml"`**: Returns conversation as a YAML string
+- **`"xml"`**: Returns conversation as an XML string
+- **`"dict-all-except-first"`**: Returns all messages except the first as a dictionary
+- **`"str-all-except-first"`**: Returns all messages except the first as a string
+- **`"dict-final"`**: Returns the final message as a dictionary
+- **`"list-final"`**: Returns the final message as a list
+
+#### Conversation Tracking
+
+All messages are automatically tracked in the conversation object with the following roles:
+
+- **`"User"`**: The original user query
+- **`"{member_name}"`**: Each council member's response (e.g., "GPT-5.1-Councilor")
+- **`"{member_name}-Evaluation"`**: Each council member's evaluation (e.g., "GPT-5.1-Councilor-Evaluation")
+- **`"Chairman"`**: The final synthesized response
 
 #### Description
 
 Executes the complete LLM Council workflow:
 
-1. **Dispatch Phase**: Sends the query to all council members in parallel using `run_agents_concurrently`
+1. **User Query Tracking**: Adds the user query to the conversation as "User" role
-2. **Collection Phase**: Collects all responses and maps them to member names
+2. **Dispatch Phase**: Sends the query to all council members in parallel using `run_agents_concurrently`
-3. **Anonymization Phase**: Creates anonymous IDs (A, B, C, D, etc.) and shuffles them to ensure anonymity
+3. **Collection Phase**: Collects all responses, maps them to member names, and adds each to the conversation with the member's name as the role
-4. **Evaluation Phase**: Each member evaluates and ranks all anonymized responses using `batched_grid_agent_execution`
+4. **Anonymization Phase**: Creates anonymous IDs (A, B, C, D, etc.) and shuffles them to ensure anonymity
-5. **Synthesis Phase**: The Chairman agent synthesizes all responses and evaluations into a final comprehensive answer
+5. **Evaluation Phase**: Each member evaluates and ranks all anonymized responses using `batched_grid_agent_execution`, then adds evaluations to the conversation with "{member_name}-Evaluation" as the role
+6. **Synthesis Phase**: The Chairman agent synthesizes all responses and evaluations into a final comprehensive answer, which is added to the conversation as "Chairman" role
+7. **Output Formatting**: Returns the conversation formatted according to the `output_type` setting using `history_output_formatter`
 
-The method provides verbose output by default, showing progress at each stage.
+The method provides verbose output by default, showing progress at each stage. All messages are tracked in the `conversation` attribute for later access or export.
 
 #### Example Usage
 
 ```python
 from swarms.structs.llm_council import LLMCouncil
 
+# Create council with default output format (dict)
 council = LLMCouncil(verbose=True)
 
 query = "What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?"
 
+# Run the council - returns formatted conversation based on output_type
 result = council.run(query)
 
-# Access the final synthesized response
+# With default "dict" output_type, result is a list of message dictionaries
-print(result["final_response"])
+# Access conversation messages
+for message in result:
+    print(f"{message['role']}: {message['content'][:200]}...")
+
+# Access the conversation object directly for more control
+conversation = council.conversation
+print("\nFinal message:", conversation.get_final_message_content())
 
-# Access individual member responses
+# Get conversation as string
-for name, response in result["original_responses"].items():
+print("\nFull conversation:")
-    print(f"{name}: {response[:200]}...")
+print(conversation.get_str())
 
-# Access evaluation rankings
+# Example with different output types
-for evaluator, evaluation in result["evaluations"].items():
+council_json = LLMCouncil(output_type="json", verbose=False)
-    print(f"{evaluator} evaluation:\n{evaluation[:300]}...")
+result_json = council_json.run(query)  # Returns JSON string
 
-# Check anonymous mapping
+council_final = LLMCouncil(output_type="final", verbose=False)
-print("Anonymous IDs:", result["anonymous_mapping"])
+result_final = council_final.run(query)  # Returns only final response string
 ```
 
 ---
@@ -225,6 +232,7 @@ Internal method that creates the default council configuration with four special
 - **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
@@ -367,25 +375,40 @@ For comprehensive examples demonstrating various use cases, see the [LLM Council
 ```python
 from swarms.structs.llm_council import LLMCouncil
 
-# Create the council
+# Create the council with default output format
 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
+# Run the council - returns formatted conversation
 result = council.run(query)
 
-# Print final response
+# With default "dict" output_type, result is a list of message dictionaries
-print(result["final_response"])
+# Print all messages
+for message in result:
+    role = message['role']
+    content = message['content']
+    print(f"\n{role}:")
+    print(content[:500] + "..." if len(content) > 500 else content)
 
-# Optionally print evaluations
+# Access conversation object directly for more options
-print("\n\n" + "="*80)
+conversation = council.conversation
-print("EVALUATIONS")
+
+# Get only the final response
+print("\n" + "="*80)
+print("FINAL RESPONSE")
 print("="*80)
-for name, evaluation in result["evaluations"].items():
+print(conversation.get_final_message_content())
-    print(f"\n{name}:")
+
-    print(evaluation[:500] + "..." if len(evaluation) > 500 else evaluation)
+# Get conversation as formatted string
+print("\n" + "="*80)
+print("FULL CONVERSATION")
+print("="*80)
+print(conversation.get_str())
+
+# Export conversation to JSON
+conversation.export()
 ```
 
 ## Customization
@@ -428,6 +451,50 @@ council = LLMCouncil(
 )
 ```
 
+### Custom Output Format
+
+You can control the output format using the `output_type` parameter:
+
+```python
+# Get output as JSON string
+council = LLMCouncil(output_type="json")
+result = council.run(query)  # Returns JSON string
+
+# Get only the final response
+council = LLMCouncil(output_type="final")
+result = council.run(query)  # Returns only final response string
+
+# Get as YAML
+council = LLMCouncil(output_type="yaml")
+result = council.run(query)  # Returns YAML string
+
+# Get as formatted string
+council = LLMCouncil(output_type="string")
+result = council.run(query)  # Returns formatted conversation string
+```
+
+### Accessing Conversation History
+
+The conversation object is accessible for advanced usage:
+
+```python
+council = LLMCouncil()
+council.run(query)
+
+# Access conversation directly
+conversation = council.conversation
+
+# Get conversation history
+history = conversation.conversation_history
+
+# Export to file
+conversation.export()  # Saves to default location
+
+# Get specific format
+json_output = conversation.to_json()
+yaml_output = conversation.return_messages_as_dictionary()
+```
+
 ## Architecture Benefits
 
 1. **Diversity**: Multiple models provide varied perspectives and approaches
@@ -436,6 +503,8 @@ council = LLMCouncil(
 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
+7. **Conversation Tracking**: All messages are automatically tracked in a Conversation object for history and export
+8. **Flexible Output**: Multiple output formats supported via `history_output_formatter` (dict, list, string, JSON, YAML, XML, etc.)
 
 ## Performance Considerations
 
@@ -443,11 +512,14 @@ council = LLMCouncil(
 - **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
+- **Conversation Management**: Conversation object efficiently tracks all messages in memory and supports export to JSON/YAML files
+- **Output Formatting**: Choose lightweight output formats (e.g., "final") for production to reduce memory usage
 
 ## 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
+- [Conversation Class Reference](conversation.md) - Understanding conversation tracking and management
 - [Multi-Agent Execution Utilities](various_execution_methods.md) - Underlying execution methods
-
+- [History Output Formatter](../../../swarms/utils/history_output_formatter.py) - Output formatting utilities

examples/multi_agent/llm_council_examples/business_strategy_council.py

@@ -29,4 +29,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/etf_stock_analysis_council.py

@@ -27,4 +27,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/finance_analysis_council.py

@@ -27,4 +27,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/legal_analysis_council.py

@@ -29,4 +29,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/marketing_strategy_council.py

@@ -26,4 +26,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/medical_diagnosis_council.py

@@ -34,4 +34,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/medical_treatment_council.py

@@ -28,4 +28,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/research_analysis_council.py

@@ -29,4 +29,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

examples/multi_agent/llm_council_examples/technology_assessment_council.py

@@ -29,4 +29,3 @@ result = council.run(query)
 
 # Print final response
 print(result["final_response"])
-

llm_council_example.py

@@ -15,5 +15,8 @@ print(result["final_response"])
 # Optionally print evaluations
 for name, evaluation in result["evaluations"].items():
     print(f"\n{name}:")
-    print(evaluation[:500] + "..." if len(evaluation) > 500 else evaluation)
+    print(
-
+        evaluation[:500] + "..."
+        if len(evaluation) > 500
+        else evaluation
+    )

swarms/agents/reasoning_agents.py

@@ -88,9 +88,7 @@ class ReasoningAgentRouter:
         eval: bool = False,
         random_models_on: bool = False,
         majority_voting_prompt: Optional[str] = None,
-        reasoning_model_name: Optional[
+        reasoning_model_name: Optional[str] = "gpt-4o",
-            str
-        ] = "gpt-4o",
     ):
         """
         Initialize the ReasoningAgentRouter with the specified configuration.

swarms/agents/reasoning_duo.py

@@ -35,9 +35,7 @@ class ReasoningDuo:
         model_names: list[str] = ["gpt-4o-mini", "gpt-4.1"],
         system_prompt: str = "You are a helpful assistant that can answer questions and help with tasks.",
         output_type: OutputType = "dict-all-except-first",
-        reasoning_model_name: Optional[
+        reasoning_model_name: Optional[str] = "gpt-4o",
-            str
-        ] = "gpt-4o",
         max_loops: int = 1,
         *args,
         **kwargs,

swarms/structs/aop.py

@@ -679,7 +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,

swarms/structs/llm_council.py

@@ -17,12 +17,14 @@ from swarms.structs.multi_agent_exec import (
     run_agents_concurrently,
     batched_grid_agent_execution,
 )
-
+from swarms.utils.history_output_formatter import HistoryOutputType, history_output_formatter
+from swarms.structs.conversation import Conversation
+from swarms.structs.swarm_id import swarm_id
 
 def get_gpt_councilor_prompt() -> str:
     """
     Get system prompt for GPT-5.1 councilor.
-    
+
     Returns:
         System prompt string for GPT-5.1 councilor agent.
     """
@@ -46,7 +48,7 @@ Remember: You are part of a council where multiple AI models will respond to the
 def get_gemini_councilor_prompt() -> str:
     """
     Get system prompt for Gemini 3 Pro councilor.
-    
+
     Returns:
         System prompt string for Gemini 3 Pro councilor agent.
     """
@@ -70,7 +72,7 @@ Remember: You are part of a council where multiple AI models will respond to the
 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.
     """
@@ -94,7 +96,7 @@ Remember: You are part of a council where multiple AI models will respond to the
 def get_grok_councilor_prompt() -> str:
     """
     Get system prompt for Grok-4 councilor.
-    
+
     Returns:
         System prompt string for Grok-4 councilor agent.
     """
@@ -118,7 +120,7 @@ Remember: You are part of a council where multiple AI models will respond to the
 def get_chairman_prompt() -> str:
     """
     Get system prompt for the Chairman agent.
-    
+
     Returns:
         System prompt string for the Chairman agent.
     """
@@ -141,23 +143,27 @@ Your approach:
 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:
+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([
+    responses_text = "\n\n".join(
-        f"Response {response_id}:\n{response_text}"
+        [
-        for response_id, response_text in responses.items()
+            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}
@@ -191,30 +197,34 @@ def get_synthesis_prompt(
     query: str,
     original_responses: Dict[str, str],
     evaluations: Dict[str, str],
-    id_to_member: 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([
+    responses_section = "\n\n".join(
-        f"=== {name} ===\n{response}"
+        [
-        for name, response in original_responses.items()
+            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()
+    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:
@@ -246,38 +256,46 @@ 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,
+        id: str = swarm_id(),
+        name: str = "LLM Council",
+        description: str = "A collaborative council of LLM agents where each member independently answers a query, reviews and ranks anonymized peer responses, and a chairman synthesizes the best elements into a final answer.",
         council_members: Optional[List[Agent]] = None,
         chairman_model: str = "gpt-5.1",
         verbose: bool = True,
+        output_type: HistoryOutputType = "dict",
     ):
         """
         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.
+            output_type: Format for the output. Options: "list", "dict", "string", "final", "json", "yaml", etc.
         """
+        self.name = name
+        self.description = description
         self.verbose = verbose
-        
+        self.output_type = output_type
+
         # 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",
@@ -289,19 +307,25 @@ class LLMCouncil:
             temperature=0.7,
         )
         
+        self.conversation = Conversation(name=f"[LLM Council] [Conversation][{name}]")
+
         if self.verbose:
-            print(f"🏛️  LLM Council initialized with {len(self.council_members)} members")
+            print(
+                f"🏛️  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})")
+                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",
@@ -312,7 +336,7 @@ class LLMCouncil:
             verbose=False,
             temperature=0.7,
         )
-        
+
         # Gemini 3 Pro Agent - Concise and processed
         gemini_agent = Agent(
             agent_name="Gemini-3-Pro-Councilor",
@@ -323,7 +347,7 @@ class LLMCouncil:
             verbose=False,
             temperature=0.7,
         )
-        
+
         # Claude Sonnet 4.5 Agent - Balanced and thoughtful
         claude_agent = Agent(
             agent_name="Claude-Sonnet-4.5-Councilor",
@@ -335,7 +359,7 @@ class LLMCouncil:
             temperature=0.0,
             top_p=None,
         )
-        
+
         # Grok-4 Agent - Creative and innovative
         grok_agent = Agent(
             agent_name="Grok-4-Councilor",
@@ -346,114 +370,135 @@ class LLMCouncil:
             verbose=False,
             temperature=0.8,
         )
-        
+
         members = [gpt_agent, gemini_agent, claude_agent, grok_agent]
-        
+
         return members
-    
+
-    def run(self, query: str) -> Dict:
+    def run(self, query: str):
         """
         Execute the full LLM Council workflow.
-        
+
         Args:
             query: The user's query to process
-            
+
         Returns:
-            Dictionary containing:
+            Formatted output based on output_type, containing conversation history
-                - original_responses: Dict mapping member names to their responses
+            with all council member responses, evaluations, and final synthesis.
-                - evaluations: Dict mapping evaluator names to their rankings
-                - final_response: The Chairman's synthesized final answer
         """
         if self.verbose:
             print(f"\n{'='*80}")
             print("🏛️  LLM COUNCIL SESSION")
-            print("="*80)
+            print("=" * 80)
             print(f"\n📝 Query: {query}\n")
-        
+
+        # Add user query to conversation
+        self.conversation.add(role="User", content=query)
+
         # Step 1: Get responses from all council members in parallel
         if self.verbose:
             print("📤 Dispatching query to all council members...")
-        
+
         results_dict = run_agents_concurrently(
             self.council_members,
             task=query,
-            return_agent_output_dict=True
+            return_agent_output_dict=True,
         )
-        
+
         # Map results to member names
         original_responses = {
             member.agent_name: response
-            for member, response in zip(self.council_members, 
+            for member, response in zip(
-                                       [results_dict.get(member.agent_name, "") 
+                self.council_members,
-                                        for member in self.council_members])
+                [
+                    results_dict.get(member.agent_name, "")
+                    for member in self.council_members
+                ],
+            )
         }
-        
+
+        # Add each council member's response to conversation
+        for member_name, response in original_responses.items():
+            self.conversation.add(role=member_name, content=response)
+
         if self.verbose:
-            print(f"✅ Received {len(original_responses)} responses\n")
+            print(
+                f"✅ 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))]
+        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("\n🔍 Council members evaluating each other's responses...")
+            print(
-        
+                "\n🔍 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)
+            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,
+            self.council_members, evaluation_tasks
-            evaluation_tasks
         )
-        
+
         # Map results to member names
         evaluations = {
             member.agent_name: evaluation_results[i]
             for i, member in enumerate(self.council_members)
         }
-        
+
+        # Add each council member's evaluation to conversation
+        for member_name, evaluation in evaluations.items():
+            self.conversation.add(
+                role=f"{member_name}-Evaluation", content=evaluation
+            )
+
         if self.verbose:
             print(f"✅ Received {len(evaluations)} evaluations\n")
-        
+
         # Step 4: Chairman synthesizes everything
         if self.verbose:
             print("👔 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)
-        
+
+        # Add chairman's final response to conversation
+        self.conversation.add(role="Chairman", content=final_response)
+
         if self.verbose:
             print(f"{'='*80}")
             print("✅ FINAL RESPONSE")
             print(f"{'='*80}\n")
-        
-        return {
-            "query": query,
-            "original_responses": original_responses,
-            "evaluations": evaluations,
-            "final_response": final_response,
-            "anonymous_mapping": id_to_member,
-        }
 
+        # Format and return output using history_output_formatter
+        return history_output_formatter(
+            conversation=self.conversation, type=self.output_type
+        )

tests/structs/test_auto_swarms_builder.py

@@ -168,7 +168,9 @@ def test_error_handling():
         # Test with invalid agent configuration
         print("Testing invalid agent configuration...")
         try:
-            swarm.create_agents_from_specs({"agents": [{"agent_name": ""}]})
+            swarm.create_agents_from_specs(
+                {"agents": [{"agent_name": ""}]}
+            )
             print(
                 "✗ Should have raised an error for empty agent configuration"
             )

tests/structs/test_i_agent.py

@@ -1,5 +1,3 @@
-import pytest
-
 from swarms.agents.i_agent import IterativeReflectiveExpansion
 
 

tests/structs/test_sequential_workflow.py

@@ -3,7 +3,6 @@ import pytest
 from swarms import Agent, SequentialWorkflow
 
 
-
 def test_sequential_workflow_initialization_with_agents():
     """Test SequentialWorkflow initialization with agents"""
     agent1 = Agent(