name: swarms-ai description: Build and orchestrate multi-agent AI systems using the Swarms API. Use when creating single agents, multi-agent swarms (sequential, concurrent, hierarchical, mixture-of-agents, majority voting, graph workflows), launching agent tokens on Solana, integrating ATP payment protocol, publishing to Swarms Marketplace, using sub-agent delegation, streaming responses, or building any multi-agent orchestration pipeline. Covers Python, TypeScript, and cURL.

Swarms AI — Multi-Agent Orchestration

Build production-grade multi-agent systems using the Swarms API platform. Supports single agents, reasoning agents, and swarms of 3–10,000+ agents with 20+ architecture patterns.

Quick Reference

• Base URL: https://api.swarms.world
• Auth: x-api-key header with API key from swarms.world/platform/api-keys
• Docs index: https://docs.swarms.ai/llms.txt
• Python SDK: pip install swarms-client
• Marketplace: swarms.world

Architecture Tiers

Workflow

1. Single Agent

import requests

payload = {
    "agent_config": {
        "agent_name": "MyAgent",
        "description": "Purpose of the agent",
        "system_prompt": "You are...",
        "model_name": "gpt-4o",  # or claude-sonnet-4-20250514, etc.
        "role": "worker",
        "max_loops": 1,
        "max_tokens": 8192,
        "temperature": 0.5,
        "auto_generate_prompt": False,
        "tools_list_dictionary": None
    },
    "task": "Your task here"
}

response = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
    json=payload
)

2. Multi-Agent Swarm

payload = {
    "name": "My Swarm",
    "description": "What this swarm does",
    "agents": [
        {
            "agent_name": "Agent1",
            "description": "Role 1",
            "system_prompt": "You are...",
            "model_name": "gpt-4o",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5
        },
        {
            "agent_name": "Agent2",
            "description": "Role 2",
            "system_prompt": "You are...",
            "model_name": "claude-sonnet-4-20250514",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5
        }
    ],
    "max_loops": 1,
    "swarm_type": "SequentialWorkflow",  # See architecture table
    "task": "Your task here"
}

response = requests.post(
    "https://api.swarms.world/v1/swarm/completions",
    headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
    json=payload
)

3. Token Launch (Solana)

payload = {
    "name": "My Agent Token",
    "description": "Agent description",
    "ticker": "MAG",
    "private_key": "[1,2,3,...]"  # Solana wallet private key
}

response = requests.post(
    "https://swarms.world/api/token/launch",
    headers={"Authorization": "Bearer API_KEY", "Content-Type": "application/json"},
    json=payload
)
# Returns: token_address, pool_address, listing_url
# Cost: ~0.04 SOL

Available Swarm Architectures

Use the swarm_type parameter:

Agent Config Parameters

Rules

• Always use environment variables for API keys — never hardcode.
• Set appropriate max_loops — use "auto" only when sub-agent delegation is needed.
• Match swarm_type to use case (see architecture table).
• For streaming, set streaming_on: true and parse SSE events (metadata → chunks → usage → done).
• Token launches cost ~0.04 SOL from the provided wallet.
• Batch endpoint (/v1/swarm/batch/completions) requires Pro/Ultra/Premium tier.
• Reasoning agents (/v1/reasoning-agent/completions) require Pro+ tier.

Resource Map

Read references only when the task requires that specific depth.