Reasoning Agent Types

const API_KEY = process.env.SWARMS_API_KEY;
const BASE_URL = "https://api.swarms.world";

const headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
};

async function getReasoningAgentTypes() {
    try {
        const response = await fetch(`${BASE_URL}/v1/reasoning-agent/types`, {
            method: 'GET',
            headers: headers
        });

        if (!response.ok) {
            throw new Error(`HTTP error! status: ${response.status}`);
        }

        const data = await response.json();
        console.log("✅ Reasoning agent types retrieved successfully!");
        console.log(JSON.stringify(data, null, 2));
        return data;
    } catch (error) {
        console.error('Error:', error);
        return null;
    }
}

// Get reasoning agent types
getReasoningAgentTypes();

# Get reasoning agent types
curl -X GET "https://api.swarms.world/v1/reasoning-agent/types" \
  -H "x-api-key: your-api-key" \
  -H "Content-Type": "application/json"

# Example response:
# {
#   "reasoning-agent": "Basic reasoning agent",
#   "reasoning-duo": "Two-agent reasoning system",
#   "self-consistency": "Multiple samples for consistency",
#   "ire": "Iterative refinement",
#   "consistency-agent": "Consistency-focused agent"
# }

payload = {
    "agent_name": "Consistent Reasoner",
    "model_name": "gpt-4o",
    "system_prompt": "You are a reasoning agent that values consistency.",
    "max_loops": 1,
    "swarm_type": "self-consistency",
    "num_samples": 3,
    "task": "Determine the most logical conclusion from these premises: All humans are mortal. Socrates is human. Therefore..."
}

payload = {
    "agent_name": "Refinement Agent",
    "model_name": "gpt-4o",
    "system_prompt": "You are an agent that improves through iteration.",
    "max_loops": 3,
    "swarm_type": "ire",
    "num_samples": 2,
    "task": "Design a more efficient algorithm for sorting a list of numbers."
}

payload = {
    "agent_name": "Dual Reasoner",
    "model_name": "gpt-4o",
    "system_prompt": "You are a dual reasoning system.",
    "max_loops": 1,
    "swarm_type": "reasoning-duo",
    "num_samples": 2,
    "task": "Debate the pros and cons of renewable energy adoption."
}

payload = {
    "agent_name": "Deep Analysis Agent",
    "model_name": "gpt-4o",
    "max_loops": 5,  # Multiple refinement iterations
    "swarm_type": "ire",
    "num_samples": 3,
    "output_type": "final",  # Return only final result
    "task": "Develop a comprehensive business strategy for a tech startup."
}

payload = {
    "agent_name": "Knowledge Agent",
    "model_name": "gpt-4o",
    "max_loops": 1,
    "swarm_type": "consistency-agent",
    "num_samples": 3,
    "num_knowledge_items": 5,  # Include knowledge base
    "memory_capacity": 10,
    "task": "Integrate these research findings into a coherent theory."
}

# Returns multiple options
payload = {
    "swarm_type": "reasoning-duo",
    "output_type": "list",
    "task": "Generate business ideas"
}
# Output: ["Idea 1", "Idea 2", "Idea 3"]

# Returns only the best result
payload = {
    "swarm_type": "ire",
    "output_type": "final",
    "task": "Solve this equation"
}
# Output: "x = 42"

def optimize_reasoning_cost(budget, quality_requirement):
    """Optimize for cost while maintaining quality"""

    if budget < 0.1:
        return {
            "swarm_type": "reasoning-agent",
            "model_name": "gpt-4o-mini",
            "num_samples": 1
        }
    elif quality_requirement == "high":
        return {
            "swarm_type": "self-consistency",
            "model_name": "gpt-4o",
            "num_samples": 3,
            "max_tokens": 2048
        }
    else:
        return {
            "swarm_type": "reasoning-duo",
            "model_name": "gpt-4o-mini",
            "num_samples": 2
        }

payload = {
    "agent_name": "Strategy Consultant",
    "swarm_type": "reasoning-duo",
    "num_samples": 2,
    "task": "Evaluate three potential market entry strategies for our product."
}

payload = {
    "agent_name": "Legal Analyst",
    "swarm_type": "self-consistency",
    "num_samples": 5,
    "task": "Analyze the legal implications of this contract clause."
}

payload = {
    "agent_name": "Technical Expert",
    "swarm_type": "consistency-agent",
    "num_samples": 3,
    "task": "Debug this software issue and propose a solution."
}