Agent Scope X Cloudflare Workers X Algolia Search X R2 Bucket Storage
Technical Assessment: Algolia + R2 Solves Core Constraints
Algolia integration addresses the R2 queryability limitation effectively. Manticore Search introduces unnecessary complexity for your current architecture needs.
Why Algolia + R2 Works
R2 for storage + Algolia for instant search creates the optimal data flow:
Store full context documents in R2 (no size limits)
Index metadata and searchable fields in Algolia
Sub-100ms query response times globally
Vector similarity search through Algolia's built-in capabilities
Why Skip Manticore Search
Manticore adds another database layer without solving constraints that Algolia doesn't already handle:
Deployment complexity: Additional server/container management
Data synchronization: Three-way sync between R2, Algolia, and Manticore
Query fragmentation: Different query languages across systems
Constraint principle: Fewer moving parts = higher reliability.
Simplified Implementation Plan
Phase 1: Core System (1 week)
AgentScope agents with constraint-led prompts
Cloudflare Worker for context injection (edge-deployed)
Algolia index for decision history search
R2 bucket for full context storage
Phase 2: Context Intelligence (1 week)
Decision logging pipeline to R2 + Algolia
Similarity search for relevant historical context
Constraint extraction from project roadmap
Alignment scoring based on constraint adherence
Phase 3: Multi-Agent Coordination (1 week)
MsgHub integration for transparent message passing
Sequential pipeline for domain processing workflow
Error handling and alignment violation detection
Performance monitoring dashboard
Critical Implementation Details
Algolia Index Structure
json
{
"objectID": "agent_001_1672531200",
"agent_id": "content_strategist",
"task": "Create content strategy for domain X",
"decision": "Focus on long-tail keywords...",
"alignment_score": 0.87,
"constraints_followed": ["2000+ words", "SEO optimized"],
"timestamp": "2024-01-01T00:00:00Z",
"domain_category": "saas",
"success_indicators": ["traffic_increase", "authority_score"]
}R2 Document Structure
json
{
"decision_id": "agent_001_1672531200",
"full_context": {
"strategic_prompt": "...",
"agent_reasoning": "...",
"constraint_analysis": "...",
"execution_steps": [...]
},
"performance_metrics": {
"execution_time": 2.3,
"context_relevance": 0.92,
"outcome_success": true
}
}Edge Context Injection Flow
User Request → Cloudflare Worker → Algolia Query (similar decisions) → R2 Fetch (full context) → Context Injection → AgentScope Agent → Decision Logging → R2 + Algolia Storage
Latency target: <200ms for context retrieval and injection globally.
Constraint Validation Logic
python
def validate_constraints(task: str, decision: str, agent_type: str) -> float:
"""Return alignment score 0-1"""
required_constraints = get_constraints_for_agent_type(agent_type)
score = 0.0
for constraint in required_constraints:
if constraint_satisfied(constraint, decision):
score += 1.0 / len(required_constraints)
# Penalty for constraint violations
violations = detect_violations(decision, required_constraints)
score -= len(violations) * 0.2
return max(0.0, min(1.0, score))Deployment Sequence
Week 1: Foundation
Deploy Cloudflare Worker for context injection
Set up Algolia index with initial schema
Create R2 bucket with lifecycle policies
Build basic AgentScope agents
Week 2: Integration
Connect agents to context injection system
Implement decision logging pipeline
Test end-to-end workflow with 3 domains
Monitor performance and alignment scores
Week 3: Scaling
Deploy to 10-20 domains
Tune constraint validation logic
Optimize context retrieval performance
Build monitoring dashboard
Reality Check: Viable 3-Week Timeline
This simplified architecture is actually achievable in 3 weeks because:
AgentScope handles agent coordination complexity
Cloudflare Workers provide zero-config edge deployment
Algolia eliminates custom search engine development
R2 requires minimal configuration
The constraint-led approach focuses on proving the core concept rather than building comprehensive enterprise features.
Start with the Cloudflare Worker context injection. That's the highest-leverage component that enables everything else.
#!/usr/bin/env python3
"""
Simplified LEVERAGEAI Multi-Agent System
Core components: AgentScope + Cloudflare Workers + Algolia + R2
"""
import asyncio
import json
from datetime import datetime
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
import hashlib
import logging
from agentscope.agents import ReActAgent
from agentscope.message import Msg
from agentscope.pipeline import MsgHub, sequential_pipeline
from sentence_transformers import SentenceTransformer
import aiohttp
@dataclass
class AgentDecision:
"""Simple decision tracking structure"""
agent_id: str
timestamp: datetime
task: str
decision: str
constraints_followed: List[str]
alignment_score: float
@dataclass
class ContextPacket:
"""Lightweight context package for agent injection"""
relevant_constraints: List[str]
similar_decisions: List[str]
roadmap_alignment: str
execution_guidance: str
class SimplifiedContextManager:
"""Lightweight context management using Algolia + R2"""
def __init__(self, algolia_app_id: str, algolia_api_key: str):
self.algolia_app_id = algolia_app_id
self.algolia_api_key = algolia_api_key
self.encoder = SentenceTransformer('all-MiniLM-L6-v2')
# Core constraints for LEVERAGEAI
self.project_constraints = {
"technical": [
"Sub-2s page load times",
"90+ PageSpeed scores",
"Zero downtime deployment"
],
"content": [
"2000+ word articles",
"Authority building focus",
"SEO optimization required"
],
"revenue": [
"Conversion-optimized funnels",
"$200K+ MRR target",
"Scalable monetization"
]
}
self.roadmap_phases = {
"foundation": "Domain network setup and optimization",
"scaling": "Content automation and authority building",
"revenue": "Monetization and client acquisition"
}
async def inject_context(self, agent_id: str, task: str) -> ContextPacket:
"""Get relevant context for agent task"""
# 1. Find relevant constraints
relevant_constraints = self._extract_relevant_constraints(task)
# 2. Query Algolia for similar successful decisions
similar_decisions = await self._query_similar_decisions(task)
# 3. Determine roadmap alignment
roadmap_alignment = self._assess_roadmap_alignment(task)
# 4. Generate execution guidance
execution_guidance = self._generate_guidance(
task, relevant_constraints, roadmap_alignment
)
return ContextPacket(
relevant_constraints=relevant_constraints,
similar_decisions=similar_decisions,
roadmap_alignment=roadmap_alignment,
execution_guidance=execution_guidance
)
def _extract_relevant_constraints(self, task: str) -> List[str]:
"""Extract constraints relevant to task"""
task_lower = task.lower()
relevant = []
for category, constraints in self.project_constraints.items():
for constraint in constraints:
# Simple keyword matching - can be enhanced
if any(word in task_lower for word in constraint.lower().split()[:2]):
relevant.append(f"{category}: {constraint}")
return relevant
async def _query_similar_decisions(self, task: str) -> List[str]:
"""Query Algolia for similar successful decisions"""
# This would be actual Algolia API call
# For now, return mock data structure
mock_decisions = [
"Domain setup: Used Cloudflare for CDN + security",
"Content strategy: Focused on long-tail keywords",
"Revenue: Implemented multi-tier pricing"
]
return mock_decisions[:2] # Limit to prevent context bloat
def _assess_roadmap_alignment(self, task: str) -> str:
"""Assess which roadmap phase task aligns with"""
task_lower = task.lower()
phase_keywords = {
"foundation": ["domain", "setup", "infrastructure", "cloudflare"],
"scaling": ["content", "automation", "seo", "article"],
"revenue": ["monetization", "revenue", "conversion", "client"]
}
for phase, keywords in phase_keywords.items():
if any(keyword in task_lower for keyword in keywords):
return f"Phase: {phase} - {self.roadmap_phases[phase]}"
return "Phase: General - Cross-phase activity"
def _generate_guidance(self, task: str, constraints: List[str],
alignment: str) -> str:
"""Generate constraint-led execution guidance"""
guidance = f"CONSTRAINT-LED APPROACH:\n"
guidance += f"1. Primary constraints: {', '.join(constraints[:2])}\n"
guidance += f"2. Roadmap alignment: {alignment}\n"
guidance += f"3. Design within constraint boundaries first\n"
guidance += f"4. Optimize for reliability over features\n"
return guidance
async def log_decision(self, decision: AgentDecision):
"""Log agent decision to R2 + Algolia for future reference"""
# Create document for Algolia indexing
algolia_doc = {
"objectID": f"{decision.agent_id}_{int(decision.timestamp.timestamp())}",
"agent_id": decision.agent_id,
"task": decision.task,
"decision": decision.decision,
"alignment_score": decision.alignment_score,
"timestamp": decision.timestamp.isoformat(),
"constraints": decision.constraints_followed
}
# Store full decision in R2, index metadata in Algolia
await self._store_to_r2(decision)
await self._index_to_algolia(algolia_doc)
async def _store_to_r2(self, decision: AgentDecision):
"""Store complete decision data to R2"""
# R2 storage implementation
decision_data = asdict(decision)
decision_data['timestamp'] = decision.timestamp.isoformat()
# Would use Cloudflare R2 API here
logging.info(f"Storing decision to R2: {decision.agent_id}")
async def _index_to_algolia(self, doc: Dict):
"""Index decision metadata to Algolia for search"""
# Algolia indexing implementation
logging.info(f"Indexing to Algolia: {doc['objectID']}")
class ConstraintLedAgent(ReActAgent):
"""AgentScope agent with constraint-led architecture"""
def __init__(self, name: str, specialization: str, context_manager: SimplifiedContextManager, **kwargs):
super().__init__(name=name, **kwargs)
self.specialization = specialization
self.context_manager = context_manager
# Override system prompt
self.sys_prompt = f"""You are {name}, specializing in {specialization}.
CORE PRINCIPLES:
1. CONSTRAINTS FIRST: Identify limitations before solutions
2. SIMPLE & ROBUST: Fewer components, higher reliability
3. DOCUMENT DECISIONS: Record reasoning for alignment tracking
4. ESCALATE CONFLICTS: Flag constraint violations immediately
You receive strategic context before each task. Use this context to make constraint-aware decisions.
Specialization: {specialization}
"""
async def reply(self, x: Msg) -> Msg:
"""Process message with constraint-led approach"""
# 1. Get strategic context
context = await self.context_manager.inject_context(self.name, x.content)
# 2. Build enhanced prompt with context
enhanced_content = self._build_contextual_prompt(x.content, context)
# 3. Create enhanced message
enhanced_msg = Msg(
name=x.name,
content=enhanced_content,
role=x.role
)
# 4. Process with parent ReAct logic
response = await super().reply(enhanced_msg)
# 5. Log decision
await self._log_agent_decision(x.content, response.content, context)
return response
def _build_contextual_prompt(self, task: str, context: ContextPacket) -> str:
"""Build prompt with injected context"""
prompt = f"""STRATEGIC CONTEXT:
RELEVANT CONSTRAINTS:
{chr(10).join(f"- {constraint}" for constraint in context.relevant_constraints)}
SIMILAR SUCCESSFUL APPROACHES:
{chr(10).join(f"- {decision}" for decision in context.similar_decisions)}
ROADMAP ALIGNMENT:
{context.roadmap_alignment}
EXECUTION GUIDANCE:
{context.execution_guidance}
CURRENT TASK: {task}
Execute with constraint-led architecture principles.
"""
return prompt
async def _log_agent_decision(self, task: str, response: str, context: ContextPacket):
"""Log decision for learning and alignment"""
decision = AgentDecision(
agent_id=self.name,
timestamp=datetime.now(),
task=task,
decision=response,
constraints_followed=context.relevant_constraints,
alignment_score=0.8 # Would calculate based on constraint adherence
)
await self.context_manager.log_decision(decision)
class CloudflareWorkerManager:
"""Manage Cloudflare Worker deployment for edge context"""
@staticmethod
def generate_context_worker() -> str:
"""Generate simplified Cloudflare Worker for context injection"""
return """
// Simplified Context Injection Worker
export default {
async fetch(request, env, ctx) {
const url = new URL(request.url);
if (url.pathname === '/context') {
return handleContextRequest(request, env);
}
return new Response('Context service active');
}
};
async function handleContextRequest(request, env) {
const { agent_id, task } = await request.json();
try {
// 1. Query Algolia for similar decisions
const algoliaResponse = await fetch(
`https://${env.ALGOLIA_APP_ID}-dsn.algolia.net/1/indexes/agent_decisions/query`,
{
method: 'POST',
headers: {
'X-Algolia-API-Key': env.ALGOLIA_SEARCH_KEY,
'X-Algolia-Application-Id': env.ALGOLIA_APP_ID,
'Content-Type': 'application/json'
},
body: JSON.stringify({
query: task,
hitsPerPage: 3,
filters: `alignment_score > 0.7`
})
}
);
const algoliaData = await algoliaResponse.json();
// 2. Build context packet
const contextPacket = {
similar_decisions: algoliaData.hits.map(hit => hit.decision),
constraints: getRelevantConstraints(task),
guidance: generateExecutionGuidance(task)
};
return new Response(JSON.stringify(contextPacket), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({ error: error.message }), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
function getRelevantConstraints(task) {
const constraints = {
domain: ['Sub-2s load time', 'SSL required'],
content: ['2000+ words', 'SEO optimized'],
revenue: ['Conversion tracking', 'Scalable pricing']
};
const taskLower = task.toLowerCase();
if (taskLower.includes('domain') || taskLower.includes('infrastructure')) {
return constraints.domain;
} else if (taskLower.includes('content') || taskLower.includes('article')) {
return constraints.content;
} else if (taskLower.includes('revenue') || taskLower.includes('monetization')) {
return constraints.revenue;
}
return ['Follow project objectives', 'Document decisions'];
}
function generateExecutionGuidance(task) {
return `
CONSTRAINT-LED APPROACH:
1. Identify limiting factors first
2. Design within constraint boundaries
3. Optimize for reliability over features
4. Document decision rationale
`;
}
"""
class SimplifiedOrchestrator:
"""Main orchestrator for simplified multi-agent system"""
def __init__(self, algolia_app_id: str, algolia_api_key: str):
self.context_manager = SimplifiedContextManager(algolia_app_id, algolia_api_key)
self.agents = {}
async def initialize_core_agents(self):
"""Initialize the three core agents"""
agent_configs = [
{
'name': 'content_agent',
'specialization': 'content_strategy_and_seo'
},
{
'name': 'infrastructure_agent',
'specialization': 'technical_infrastructure'
},
{
'name': 'revenue_agent',
'specialization': 'revenue_optimization'
}
]
for config in agent_configs:
agent = ConstraintLedAgent(
name=config['name'],
specialization=config['specialization'],
context_manager=self.context_manager,
model_config_name="claude_config"
)
self.agents[config['name']] = agent
logging.info("Initialized 3 core agents with constraint-led architecture")
async def execute_domain_task(self, domain: str, task_type: str) -> Dict:
"""Execute single domain task with appropriate agent"""
agent_mapping = {
'content': 'content_agent',
'infrastructure': 'infrastructure_agent',
'revenue': 'revenue_agent'
}
agent_name = agent_mapping.get(task_type, 'content_agent')
agent = self.agents[agent_name]
task = Msg(
name="orchestrator",
content=f"Execute {task_type} optimization for domain: {domain}",
role="user"
)
result = await agent.reply(task)
return {
'domain': domain,
'task_type': task_type,
'agent': agent_name,
'result': result.content,
'timestamp': datetime.now().isoformat()
}
async def process_domain_batch(self, domains: List[str]) -> Dict:
"""Process batch of domains through simplified pipeline"""
results = {}
for domain in domains:
domain_results = {}
# Execute each task type for domain
task_types = ['content', 'infrastructure', 'revenue']
for task_type in task_types:
result = await self.execute_domain_task(domain, task_type)
domain_results[task_type] = result
logging.info(f"Completed {task_type} for {domain}")
results[domain] = domain_results
return results
# Deployment helper
class DeploymentManager:
"""Manage deployment of simplified system components"""
@staticmethod
async def deploy_cloudflare_worker(account_id: str, api_token: str):
"""Deploy context injection worker to Cloudflare"""
worker_script = CloudflareWorkerManager.generate_context_worker()
# Deploy to Cloudflare via API
async with aiohttp.ClientSession() as session:
response = await session.put(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/workers/scripts/context-injector",
headers={
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/javascript"
},
data=worker_script
)
if response.status == 200:
logging.info("Context injection worker deployed successfully")
return True
else:
logging.error(f"Worker deployment failed: {response.status}")
return False
@staticmethod
def setup_algolia_index():
"""Setup Algolia index for agent decisions"""
index_config = {
"searchableAttributes": [
"task",
"decision",
"agent_id"
],
"attributesForFaceting": [
"agent_id",
"alignment_score",
"timestamp"
],
"customRanking": [
"desc(alignment_score)",
"desc(timestamp)"
]
}
logging.info("Algolia index configuration ready")
return index_config
# Usage example
async def main():
"""Example usage of simplified system"""
# Initialize orchestrator
orchestrator = SimplifiedOrchestrator(
algolia_app_id="your_app_id",
algolia_api_key="your_api_key"
)
await orchestrator.initialize_core_agents()
# Process batch of domains
test_domains = ["leverageai.com", "automate-everything.com"]
results = await orchestrator.process_domain_batch(test_domains)
print(f"Processed {len(test_domains)} domains:")
for domain, domain_results in results.items():
print(f"\n{domain}:")
for task_type, result in domain_results.items():
print(f" {task_type}: {result['result'][:100]}...")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
asyncio.run(main())