LLM API Gateway for Multi-Agent Systems

Intelligent Orchestration Features

Comprehensive capabilities for managing multi-agent LLM interactions at scale.

🎯

Smart Task Routing

Intelligent request routing directs tasks to the most appropriate agent based on specialization, current workload, and historical performance. The gateway maintains agent capability profiles and optimizes task distribution for maximum efficiency and quality. Priority-based queuing ensures critical tasks receive immediate attention while load balancing prevents any single agent from becoming overwhelmed.

💬

Inter-Agent Communication

Structured communication protocols enable agents to share insights, request assistance, and collaborate on complex problems. The gateway provides message queuing, context sharing, and conversation threading to maintain coherent multi-agent dialogues. Built-in conflict resolution mechanisms prevent contradictory outputs when multiple agents contribute to shared work products.

🧠

Shared Memory & Context

Persistent memory systems allow agents to access shared knowledge bases, project context, and historical interactions. The gateway manages context windows efficiently, ensuring each agent receives relevant background information without exceeding token limits. Episodic memory enables agents to learn from past collaborations and improve future performance.

📊

Scalable Coordination

Horizontal scaling supports growing agent teams from small collaborative groups to enterprise-wide deployments. The gateway handles concurrent agent sessions, manages resource allocation, and provides centralized monitoring across the entire agent ecosystem. Auto-scaling capabilities adjust capacity based on workload patterns and agent activity levels.

Multi-Agent Coordination Flow

Understanding how the gateway orchestrates complex multi-agent workflows.

Task Reception

Gateway receives complex task and analyzes requirements for agent assignment

Agent Selection

Intelligent matching selects optimal agent team based on capabilities

Execution Orchestration

Coordinates parallel and sequential agent operations with context sharing

Result Synthesis

Aggregates outputs from multiple agents into cohesive final deliverable

Implementation Guide

Practical examples for building multi-agent systems with the LLM gateway.

multi_agent_orchestrator.py Python

# Multi-Agent LLM Gateway Implementation
from typing import List, Dict, Any
from dataclasses import dataclass
import asyncio

@dataclass
class Agent:
    """Agent profile with capabilities and state"""
    id: str
    name: str
    specialization: List[str]
    current_load: float = 0.0
    performance_score: float = 1.0

class MultiAgentGateway:
    """LLM Gateway for multi-agent orchestration"""
    
    def __init__(self, gateway_url: str):
        self.gateway_url = gateway_url
        self.agents: Dict[str, Agent] = {}
        self.shared_memory: Dict[str, Any] = {}
        self.conversation_threads: Dict[str, List] = {}
    
    def register_agent(self, agent: Agent):
        """Register agent with capability profile"""
        self.agents[agent.id] = agent
        print(f"Agent '{agent.name}' registered with specializations: {agent.specialization}")
    
    async def orchestrate_task(
        self, 
        task: str, 
        required_capabilities: List[str]
    ) -> Dict[str, Any]:
        """Select and coordinate agents for complex task"""
        
        # Select best agents based on capabilities and load
        selected_agents = self._select_agents(
            required_capabilities,
            max_agents=5
        )
        
        if not selected_agents:
            raise ValueError("No suitable agents available")
        
        # Create shared context for this task
        task_id = generate_task_id()
        self.shared_memory[task_id] = {
            "task": task,
            "context": {},
            "partial_results": []
        }
        
        # Coordinate agent execution
        results = await self._coordinate_agents(
            task_id=task_id,
            agents=selected_agents,
            task=task
        )
        
        # Synthesize final output
        final_output = await self._synthesize_results(results)
        
        return {
            "task_id": task_id,
            "agents_used": [a.name for a in selected_agents],
            "output": final_output
        }
    
    def _select_agents(
        self, 
        capabilities: List[str],
        max_agents: int
    ) -> List[Agent]:
        """Score and select best agents for required capabilities"""
        scored_agents = []
        
        for agent in self.agents.values():
            # Calculate capability match score
            match_score = sum(
                1 for cap in capabilities
                if cap in agent.specialization
            ) / len(capabilities)
            
            # Factor in load and performance
            final_score = (
                match_score * 0.5 +
                (1 - agent.current_load) * 0.3 +
                agent.performance_score * 0.2
            )
            
            scored_agents.append((agent, final_score))
        
        # Return top agents
        scored_agents.sort(key=lambda x: x[1], reverse=True)
        return [agent for agent, _ in scored_agents[:max_agents]]
    
    async def _coordinate_agents(
        self,
        task_id: str,
        agents: List[Agent],
        task: str
    ) -> List[Dict]:
        """Execute task across multiple agents with coordination"""
        tasks = []
        
        for agent in agents:
            # Prepare agent-specific prompt with shared context
            prompt = self._build_agent_prompt(
                agent=agent,
                task=task,
                shared_context=self.shared_memory[task_id]
            )
            
            # Dispatch to gateway
            tasks.append(
                self._dispatch_to_gateway(agent.id, prompt)
            )
        
        # Execute in parallel
        results = await asyncio.gather(*tasks)
        
        # Update shared memory
        self.shared_memory[task_id]["partial_results"].extend(results)
        
        return results

# Usage example
async def main():
    gateway = MultiAgentGateway("https://llm-gateway.example.com")
    
    # Register specialized agents
    gateway.register_agent(Agent(
        id="planner-01",
        name="Planner",
        specialization=["task_decomposition", "scheduling"]
    ))
    
    gateway.register_agent(Agent(
        id="researcher-01",
        name="Researcher",
        specialization=["information_retrieval", "analysis"]
    ))
    
    # Orchestrate complex task
    result = await gateway.orchestrate_task(
        task="Create comprehensive market analysis report",
        required_capabilities=["research", "analysis", "writing"]
    )

Real-World Applications

Multi-agent systems transforming complex workflows across industries.

🔬

Research & Analysis

Researcher agents gather information from multiple sources, analyst agents identify patterns and insights, and writer agents synthesize findings into comprehensive reports with proper citations and evidence.

💻

Software Development

Planner agents break down features into tasks, coder agents implement components, tester agents verify functionality, and reviewer agents ensure code quality and adherence to standards.

🎯

Decision Support

Analyst agents evaluate options from different perspectives, critic agents identify risks and weaknesses, and advisor agents synthesize balanced recommendations with supporting rationale.