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LLM Council: 3-Step Quickstart Guide

The LLM Council enables collaborative decision-making with multiple AI agents through peer review and synthesis. Inspired by Andrej Karpathy's llm-council, it creates a council of specialized agents that respond independently, review each other's anonymized responses, and have a Chairman synthesize the best elements into a final answer.

Overview

Feature	Description
Multiple Perspectives	Each council member provides unique insights from different viewpoints
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

Step 1: Install and Import

First, ensure you have Swarms installed and import the LLMCouncil class:

pip install swarms

from swarms.structs.llm_council import LLMCouncil

Step 2: Create the Council

Create an LLM Council with default council members (GPT-5.1, Gemini 3 Pro, Claude Sonnet 4.5, and Grok-4):

# Create the council with default members
council = LLMCouncil(
    name="Decision Council",
    verbose=True,
    output_type="dict-all-except-first"
)

Step 3: Run a Query

Execute a query and get the synthesized response:

# Run a query
result = council.run("What are the key factors to consider when choosing a cloud provider for enterprise applications?")

# Access the final synthesized answer
print(result["final_response"])

# View individual member responses
print(result["original_responses"])

# See how members ranked each other
print(result["evaluations"])

Complete Example

Here's a complete working example:

from swarms.structs.llm_council import LLMCouncil

# Step 1: Create the council
council = LLMCouncil(
    name="Strategy Council",
    description="A council for strategic decision-making",
    verbose=True,
    output_type="dict-all-except-first"
)

# Step 2: Run a strategic query
result = council.run(
    "Should a B2B SaaS startup prioritize product-led growth or sales-led growth? "
    "Consider factors like market size, customer acquisition costs, and scalability."
)

# Step 3: Process results
print("=" * 50)
print("FINAL SYNTHESIZED ANSWER:")
print("=" * 50)
print(result["final_response"])

Custom Council Members

For specialized domains, create custom council members:

from swarms import Agent
from swarms.structs.llm_council import LLMCouncil, get_gpt_councilor_prompt

# Create specialized agents
finance_expert = Agent(
    agent_name="Finance-Councilor",
    system_prompt="You are a financial analyst specializing in market analysis and investment strategies...",
    model_name="gpt-4.1",
    max_loops=1,
)

tech_expert = Agent(
    agent_name="Technology-Councilor", 
    system_prompt="You are a technology strategist specializing in digital transformation...",
    model_name="gpt-4.1",
    max_loops=1,
)

risk_expert = Agent(
    agent_name="Risk-Councilor",
    system_prompt="You are a risk management expert specializing in enterprise risk assessment...",
    model_name="gpt-4.1",
    max_loops=1,
)

# Create council with custom members
council = LLMCouncil(
    council_members=[finance_expert, tech_expert, risk_expert],
    chairman_model="gpt-4.1",
    verbose=True
)

result = council.run("Evaluate the risk-reward profile of investing in AI infrastructure")

CLI Usage

Run LLM Council directly from the command line:

swarms llm-council --task "What is the best approach to implement microservices architecture?"

With verbose output:

swarms llm-council --task "Analyze the pros and cons of remote work" --verbose

Use Cases

Domain	Example Query
Business Strategy	"Should we expand internationally or focus on domestic growth?"
Technology	"Which database architecture best suits our high-throughput requirements?"
Finance	"Evaluate investment opportunities in the renewable energy sector"
Healthcare	"What treatment approaches should be considered for this patient profile?"
Legal	"What are the compliance implications of this data processing policy?"

Next Steps

Explore LLM Council Examples for domain-specific implementations
Learn about LLM Council Reference Documentation for complete API details
Try the CLI Reference for DevOps integration

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