API Architecture
The Swarms API provides a comprehensive multi-tier architecture for building collaborative agentic systems.
Swarms API Architecture
The Swarms API provides a comprehensive multi-tier architecture for building intelligent AI systems. The platform is designed around three distinct agent paradigms, each optimized for different types of tasks and complexity levels.
Architecture Tiers
Tier 1
Individual Agents
1
Low-Medium
Focused tasks
Content generation, data analysis, Q&A
/v1/agent/completions
Tier 2
Reasoning Agents
1-2 (internal)
Medium-High
Complex reasoning
Mathematical proofs, logical validation, research
/v1/reasoning-agent/completions
Tier 3
Multi-Agent Swarms
3-10,000+
High
Enterprise workflows
Process automation, large-scale systems, R&D
/v1/swarm/completions
Tier 1: Individual Agents
Single-purpose AI agents for focused tasks
Individual agents are the foundation of the Swarms ecosystem. These are custom-built, single-purpose AI agents designed to handle specific tasks with high precision and efficiency.
Key Characteristics
Single Agent: One AI model per agent
Focused Purpose: Specialized for specific tasks
Customizable: Full control over system prompts, tools, and behavior
Efficient: Optimized for direct task execution
Scalable: Can be combined into larger systems
Use Cases
Content generation (articles, code, reports)
Data analysis and processing
Customer service responses
Creative tasks (writing, design)
Simple Q&A and information retrieval
Tool execution and automation
Example Implementation
import requests
payload = {
"agent_config": {
"agent_name": "content-writer",
"description": "Professional content writer for technical articles",
"system_prompt": "You are an expert technical writer...",
"model_name": "gpt-4o",
"max_tokens": 4000,
"temperature": 0.7
},
"task": "Write a comprehensive guide on API security best practices"
}
response = requests.post(
"https://api.swarms.world/v1/agent/completions",
headers={"x-api-key": "your-api-key"},
json=payload
)Tier 2: Reasoning Agents
Advanced reasoning systems for complex problem-solving
Reasoning agents leverage sophisticated reasoning techniques to solve complex problems that require deep analysis, multiple perspectives, and systematic thinking. These agents may internally use 1-2 specialized sub-agents to achieve their reasoning goals.
Key Characteristics
Reasoning-Focused: Built for complex logical and analytical tasks
Multi-Perspective: Can approach problems from different angles
Iterative: Capable of refinement and improvement cycles
Specialized Types: 7 different reasoning agent types available
Internal Coordination: May use sub-agents for specialized reasoning
Available Reasoning Agent Types
reasoning-duo
Dual-agent system with perspective synthesis
Mathematical problems, logical proofs
self-consistency
Multiple reasoning paths with validation
Complex logical problems, consistency checking
ire
Iterative refinement approach
Complex analysis, research problems
reasoning-agent
General-purpose systematic reasoning
Step-by-step problem solving
consistency-agent
Logical consistency and contradiction detection
Argument validation
ReflexionAgent
Self-reflection and bias detection
Meta-cognitive tasks
GKPAgent
Cross-domain knowledge synthesis
Interdisciplinary problems
Use Cases
Mathematical proofs and complex calculations
Logical consistency validation
Research and analysis tasks
Cross-domain problem solving
Bias detection and ethical analysis
Iterative improvement scenarios
Example Implementation
payload = {
"agent_name": "math-reasoner",
"description": "Mathematical problem solver using dual perspectives",
"model_name": "claude-3-5-sonnet-20240620",
"system_prompt": "You are an expert mathematical reasoning agent...",
"max_loops": 1,
"swarm_type": "reasoning-duo",
"task": "Prove that the sum of any three consecutive integers is divisible by 3"
}
response = requests.post(
"https://api.swarms.world/v1/reasoning-agent/completions",
headers={"x-api-key": "your-api-key"},
json=payload
)📖 Complete Reasoning Agents Documentation
Tier 3: Multi-Agent Swarms
Large-scale agent systems for complex workflows
Multi-agent swarms represent the most sophisticated tier, capable of orchestrating anywhere from 3 to 10,000+ agents working together in coordinated workflows. These systems are designed for enterprise-scale applications and complex business processes.
Key Characteristics
Massive Scale: 3 to 10,000+ agents per swarm
Coordinated Workflows: Agents work together in structured processes
Multiple Swarm Types: 12+ different swarm architectures available
Enterprise-Grade: Built for complex business applications
Dynamic Routing: Intelligent task distribution and agent selection
Available Swarm Types
SequentialWorkflow
Linear task progression
3-50
Process automation, step-by-step workflows
ConcurrentWorkflow
Parallel task execution
5-100
Parallel processing, independent tasks
GroupChat
Interactive agent discussions
3-20
Collaborative problem solving, brainstorming
MixtureOfAgents
Specialized agent selection
5-200
Complex tasks requiring multiple expertise areas
MajorityVoting
Consensus-based decision making
5-50
Decision making, validation tasks
CouncilAsAJudge
Expert panel with final judge
5-30
Expert evaluation, quality assessment
InteractiveGroupChat
Real-time agent interactions
3-15
Dynamic problem solving, real-time collaboration
AgentRearrange
Dynamic agent reordering
3-100
Adaptive workflows, optimization
MultiAgentRouter
Intelligent task routing
10-500
Large-scale task distribution
HiearchicalSwarm
Nested agent hierarchies
10-1000
Complex organizational structures
AutoSwarmBuilder
Automatic swarm construction
5-200
Dynamic swarm creation, optimization
MALT
Multi-agent learning and training
10-10000+
Large-scale learning systems
Use Cases
Enterprise process automation
Large-scale data processing
Complex decision-making systems
Research and development workflows
Customer service automation
Content creation pipelines
Quality assurance systems
Dynamic resource allocation
Example Implementation
payload = {
"name": "Enterprise Content Pipeline",
"description": "Multi-stage content creation and review system",
"agents": [
{
"agent_name": "researcher",
"description": "Research and gather information",
"model_name": "gpt-4o",
"role": "researcher"
},
{
"agent_name": "writer",
"description": "Create initial content",
"model_name": "claude-3-5-sonnet-20240620",
"role": "writer"
},
{
"agent_name": "editor",
"description": "Review and improve content",
"model_name": "gpt-4o",
"role": "editor"
},
{
"agent_name": "fact-checker",
"description": "Verify accuracy and sources",
"model_name": "claude-3-5-sonnet-20240620",
"role": "validator"
}
],
"max_loops": 2,
"swarm_type": "SequentialWorkflow",
"task": "Create a comprehensive industry report on AI trends in 2024"
}
response = requests.post(
"https://api.swarms.world/v1/swarm/completions",
headers={"x-api-key": "your-api-key"},
json=payload
)Architecture Comparison
Agent Count
1
1-2 (internal)
3-10,000+
Complexity
Low-Medium
Medium-High
High-Extreme
Use Case
Focused tasks
Complex reasoning
Enterprise workflows
Setup Time
Minutes
Minutes-Hours
Hours-Days
Resource Usage
Low
Medium
High
Scalability
Individual
Limited
Massive
Cost
Low
Medium
High
Maintenance
Simple
Moderate
Complex
Choosing the Right Architecture
When to Use Individual Agents
✅ Single, well-defined tasks
✅ Quick prototyping and testing
✅ Resource-constrained environments
✅ Simple automation needs
✅ Cost-sensitive applications
When to Use Reasoning Agents
✅ Complex problem-solving tasks
✅ Tasks requiring multiple perspectives
✅ Logical consistency validation
✅ Research and analysis work
✅ Tasks requiring iterative improvement
When to Use Multi-Agent Swarms
✅ Complex business processes
✅ Large-scale automation
✅ Multi-step workflows
✅ Enterprise applications
✅ Tasks requiring multiple expertise areas
✅ Dynamic, adaptive systems
Integration Patterns
Hybrid Approaches
You can combine different tiers for optimal results:
Individual + Reasoning: Use individual agents for data collection, reasoning agents for analysis
Reasoning + Swarms: Use reasoning agents within swarms for complex decision-making
All Three Tiers: Individual agents for data processing, reasoning agents for analysis, swarms for orchestration
Migration Paths
Start Simple: Begin with individual agents, upgrade to reasoning agents for complex tasks
Scale Up: Move from reasoning agents to swarms for enterprise needs
Optimize: Use reasoning agents within swarms for enhanced decision-making
Performance Considerations
Individual Agents
Latency: 1-5 seconds
Throughput: High (1000+ requests/minute)
Cost: $0.01-0.10 per request
Memory: Minimal
Reasoning Agents
Latency: 5-30 seconds
Throughput: Medium (100-500 requests/minute)
Cost: $0.05-0.50 per request
Memory: Moderate
Multi-Agent Swarms
Latency: 30 seconds - 10 minutes
Throughput: Variable (10-100 requests/minute)
Cost: $0.10-5.00 per request
Memory: High
Best Practices
1. Start with the Right Tier
Begin with individual agents for simple tasks
Upgrade to reasoning agents when complexity increases
Use swarms only when necessary for scale
2. Optimize for Your Use Case
Match agent capabilities to task requirements
Consider cost vs. performance trade-offs
Plan for scalability from the start
3. Monitor and Iterate
Track performance metrics across all tiers
Optimize based on usage patterns
Consider hybrid approaches for complex needs
Getting Started
Quick Start Guide
Get API Key: https://swarms.world/platform/api-keys
Choose Your Tier: Start with individual agents for simple tasks
Build and Test: Create your first agent and test functionality
Scale Up: Move to reasoning agents or swarms as needed
Documentation Resources
Individual Agents: Agents Overview
Reasoning Agents: Reasoning Agents Documentation
Multi-Agent Swarms: Swarm Types and Configurations
API Reference: Complete API Documentation
Support and Community
Technical Support: Book a Call
Community: Join our Discord
Updates: Follow us on Twitter
For enterprise deployments and custom solutions, contact our team for dedicated support and consultation.
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