Integrate LLMCouncil into SwarmRouter

docs/swarms/structs/swarm_router.md

@@ -42,6 +42,7 @@ Main class for routing tasks to different swarm types.
 | `verbose` | bool | Flag to enable/disable verbose logging (default: False) |
 | `worker_tools` | List[Callable] | List of tools available to worker agents |
 | `aggregation_strategy` | str | Aggregation strategy for HeavySwarm (default: "synthesis") |
+| `chairman_model` | str | Model name for the Chairman in LLMCouncil (default: "gpt-5.1") |
 
 ### Methods
 
@@ -123,6 +124,7 @@ The `SwarmRouter` supports many various multi-agent architectures for various ap
 | `InteractiveGroupChat` | Interactive group chat with user participation |
 | `HeavySwarm` | Heavy swarm architecture with question and worker agents |
 | `BatchedGridWorkflow` | Batched grid workflow for parallel task processing |
+| `LLMCouncil` | Council of specialized LLM agents with peer review and synthesis |
 | `auto` | Automatically selects best swarm type via embedding search |
 
 ## Basic Usage
@@ -456,6 +458,30 @@ result = batched_grid_router.run(tasks=["Task 1", "Task 2", "Task 3"])
 
 BatchedGridWorkflow is designed for efficiently processing multiple tasks in parallel batches, optimizing resource utilization.
 
+### LLMCouncil
+
+Use Case: Collaborative analysis with multiple specialized LLM agents that evaluate each other's responses and synthesize a final answer.
+
+```python
+llm_council_router = SwarmRouter(
+    name="LLMCouncil",
+    description="Collaborative council of LLM agents with peer review",
+    swarm_type="LLMCouncil",
+    chairman_model="gpt-5.1",  # Model for the Chairman agent
+    output_type="dict",  # Output format: "dict", "list", "string", "json", "yaml", "final", etc.
+    verbose=True  # Show progress and intermediate results
+)
+
+result = llm_council_router.run("What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?")
+```
+
+LLMCouncil creates a council of specialized agents (GPT-5.1, Gemini, Claude, Grok by default) that:
+1. Each independently responds to the query
+2. Evaluates and ranks each other's anonymized responses
+3. A Chairman synthesizes all responses and evaluations into a final comprehensive answer
+
+The council automatically tracks all messages in a conversation object and supports flexible output formats. Note: LLMCouncil uses default council members and doesn't require the `agents` parameter.
+
 ## Advanced Features
 
 ### Processing Documents

swarms/structs/llm_council.py

@@ -502,3 +502,15 @@ class LLMCouncil:
         return history_output_formatter(
             conversation=self.conversation, type=self.output_type
         )
+
+    def batched_run(self, tasks: List[str]):
+        """
+        Run the LLM Council workflow for a batch of tasks.
+
+        Args:
+            tasks: List of tasks to process
+
+        Returns:
+            List of formatted outputs based on output_type
+        """
+        return [self.run(task) for task in tasks]

swarms/structs/swarm_router.py

@@ -37,6 +37,7 @@ from swarms.telemetry.log_executions import log_execution
 from swarms.utils.generate_keys import generate_api_key
 from swarms.utils.loguru_logger import initialize_logger
 from swarms.utils.output_types import OutputType
+from swarms.structs.llm_council import LLMCouncil
 
 logger = initialize_logger(log_folder="swarm_router")
 
@@ -56,6 +57,7 @@ SwarmType = Literal[
     "InteractiveGroupChat",
     "HeavySwarm",
     "BatchedGridWorkflow",
+    "LLMCouncil",
 ]
 
 
@@ -210,6 +212,7 @@ class SwarmRouter:
         verbose: bool = False,
         worker_tools: List[Callable] = None,
         aggregation_strategy: str = "synthesis",
+        chairman_model: str = "gpt-5.1",
         *args,
         **kwargs,
     ):
@@ -252,7 +255,8 @@ class SwarmRouter:
         self.heavy_swarm_swarm_show_output = (
             heavy_swarm_swarm_show_output
         )
-
+        self.chairman_model = chairman_model
+        
         # Initialize swarm factory for O(1) lookup performance
         self._swarm_factory = self._initialize_swarm_factory()
         self._swarm_cache = {}  # Cache for created swarms
@@ -425,6 +429,7 @@ class SwarmRouter:
             "SequentialWorkflow": self._create_sequential_workflow,
             "ConcurrentWorkflow": self._create_concurrent_workflow,
             "BatchedGridWorkflow": self._create_batched_grid_workflow,
+            "LLMCouncil": self._create_llm_council,
         }
 
     def _create_heavy_swarm(self, *args, **kwargs):
@@ -441,6 +446,16 @@ class SwarmRouter:
             aggregation_strategy=self.aggregation_strategy,
             show_dashboard=False,
         )
+        
+    def _create_llm_council(self, *args, **kwargs):
+        """Factory function for LLMCouncil."""
+        return LLMCouncil(
+            name=self.name,
+            description=self.description,
+            output_type=self.output_type,
+            verbose=self.verbose,
+            chairman_model=self.chairman_model,
+        )
 
     def _create_agent_rearrange(self, *args, **kwargs):
         """Factory function for AgentRearrange."""