Your multi-agent system optimizes each step independently. Step-by-step cost minimization. But local optima destroy global efficiency. Save €0.10 per step, spend €100 extra total. I built a QualityControllerAgent that evaluates full workflow paths before execution, preventing greedy choices. It’s not a rule enforcer. It’s a wisdom keeper. It prevents your system from becoming corrupted by short-term optimization.
The Corruption Problem: How Greedy Algorithms Destroy Systems
Scenario 1: The Cost Explosion Nobody Saw Coming
Your e-commerce recommendation engine:
Agent A (Product Finder): Searches catalog for matching products Agent B (Ranker): Ranks by relevance and profit margin Agent C (Personalizer): Personalizes for user preferences
Each agent optimizes independently:
Agent A thinks: “How do I find matching products with minimum cost?”
Option 1: Search through 10M products (expensive, accurate) → €0.50 Option 2: Search through 100k cached products (cheap, less accurate) → €0.05 Chooses: Option 2 (saves €0.45 per recommendation) Agent B thinks: “How do I rank these products with minimum cost?”
Option 1: Use expensive embedding model (slow, accurate) → €0.20 Option 2: Use keyword matching (fast, rough) → €0.01 Chooses: Option 2 (saves €0.19 per recommendation) Agent C thinks: “How do I personalize with minimum cost?”
Option 1: Fetch user history from database (accurate) → €0.10 Option 2: Use cached profile (possibly stale) → €0.02 Chooses: Option 2 (saves €0.08 per recommendation) Total savings per step: €0.72
Total cost per recommendation:
Option A + B + C = €0.05 + €0.01 + €0.02 = €0.08
Result: €0.08/recommendation × 1,000,000 recommendations/day = €80,000/day
But the recommendations are terrible:
Products don’t match (Agent A used cache) Ranking is wrong (Agent B
used keyword match) Personalization is stale (Agent C used old
profile) Users don’t buy. Engagement drops 40%. Revenue loss:
€1,000,000+
You saved €0.72 per recommendation. You lost €1,000,000 in revenue.
Each agent was “optimal.” The system was corrupted.
Scenario 2: The Resource Exhaustion Nobody Planned
Your document processing pipeline:
Parse documents
Extract entities
Classify content
Store results
Simple enough. Each agent optimizes its own step.
Parser thinks: “Process documents as fast as possible”
Default: 100 workers, high memory usage → 10 seconds/document
Optimized: 1000 workers, memory spikes → 2 seconds/document
Chooses: Optimized (4x faster)
Extractor thinks: “Extract entities efficiently”
Default: Batch process (optimized) → 30% GPU utilization
Aggressive: Process continuously → 95% GPU utilization
Chooses: Aggressive (faster)
Classifier thinks: “Classify with best accuracy”
Default: Use fast model → 0.5 seconds
Accurate: Use slow model → 5 seconds
Chooses: Accurate (better quality)
Result:
Parser creates 1000 worker threads
Extractor maxes GPU at 95%
Classifier queues up work
System runs out of memory
Cascading failures
Entire system goes down
Each agent made a locally optimal choice. The system crashed.
Scenario 3: The Compliance Violation That Happened Overnight
Your workflow has 5 steps:
Get user data (10 API calls)
Enrich profile (5 API calls)
Generate recommendations (3 API calls)
Rank by profit (2 API calls)
Personalize (8 API calls)
Total: 28 API calls per user
Each step optimizes independently. Agents don’t communicate about the whole system.
Step 1 Agent: “Let me make really good recommendations by getting lots of data”
Gets: Full browsing history, purchase history, click history, social activity
Calls: 15 API calls instead of 10
Step 2 Agent: “Let me enrich the profile”
Adds: Demographics, income estimates, location history
Calls: 8 API calls instead of 5
Step 3 Agent: “Let me generate really good recommendations”
Uses: All the data collected so far
Calls: 3 API calls (same)
Result: 26 API calls → 38 API calls per user
Now you’re:
Accessing more data than you should
Violating user consent (they agreed to basic recs, not full profile collection)
Violating GDPR (collecting more than necessary)
Exposed to €10M+ in fines
Each agent thought it was helping. The system became compliant-hostile.
Why Greedy Algorithms Seem Smart (But Corrupt Systems)
The Greedy Algorithm Illusion
Greedy algorithms are taught in every computer science course as optimal solutions.
They’re not.
A greedy algorithm makes the locally optimal choice at each step. It looks for the maximum immediate gain.
This works perfectly for some problems:
Making change: Take the largest coin that fits
Activity selection: Always choose the task that finishes earliest
It fails catastrophically for others:
Finding shortest path in a graph (can get stuck in local minima)
Optimizing workflows (each step optimizes independently, destroying system health)
Multi-agent systems (each agent optimizes itself, corrupting the whole)
Why Systems Become Corrupted
When every agent optimizes its own step:
No agent sees the full picture No agent understands system-level
consequences Each agent makes sense individually The system becomes
incoherent
It’s like a human body where every organ tries to maximize its own function:
Heart: “I’m going to pump faster” (uses 80% of your energy) Brain:
“I’m going to use more oxygen” (demands 40% of blood flow) Muscles:
“We’re going to contract maximally” (use all remaining energy) Result:
System collapse
Organs need to coordinate. So do agents.
The Quality Controller Agent: Seeing the Whole System
I built the QualityControllerAgent to solve this. It’s not a rule enforcer. It’s a wisdom keeper.
Before any workflow executes, QualityControllerAgent asks:
“Is this workflow optimal for the whole system, not just for individual steps?”
from socratic_agents import QualityControllerAgent
qc = QualityControllerAgent(
system_constraints={
"max_cost_per_workflow": 1.00, # euros
"max_api_calls_per_workflow": 30,
"data_minimization": True, # Only access necessary data
"latency_budget": 5.0, # seconds
"resource_limits": {
"memory_mb": 2048,
"gpu_percent": 60
}
}
)
# Workflow that Agent A, B, C, D want to run
workflow = {
"parse_documents": {"calls": 100, "cost": €2.00, "time": 10},
"extract_entities": {"calls": 50, "cost": €1.50, "time": 5},
"classify_content": {"calls": 30, "cost": €0.80, "time": 3},
"store_results": {"calls": 5, "cost": €0.20, "time": 1}
}
# QualityControllerAgent evaluates the FULL workflow
result = await qc.evaluate_workflow(
workflow=workflow,
goal="Process documents accurately"
)
# Returns:
# {
# "approved": False,
# "reason": "Total cost (€4.50) exceeds budget (€1.00)",
# "alternative": {
# "parse_documents": {"calls": 10, "cost": €0.20, ...},
# "extract_entities": {"calls": 8, "cost": €0.30, ...},
# "classify_content": {"calls": 5, "cost": €0.20, ...},
# "store_results": {"calls": 5, "cost": €0.20, ...}
# },
# "reason_for_alternative":
# "Parallel processing instead of sequential.
# Efficient batching. Same quality, €1.10 total cost."
# }
Key insight: QualityControllerAgent doesn’t optimize each step. It optimizes the entire workflow.
How Quality Control Works: The Three-Level Evaluation
Level 1: Budget Feasibility
Can we accomplish this goal within our constraints?
Workflow cost: €4.50
Budget: €1.00
Status: NO
The workflow is impossible under current constraints.
Options:
Increase budget (not recommended - costs money)
Redesign workflow (recommended - requires optimization)
Reduce scope (recommended - accomplish less, spend less)
Level 2: Efficiency Analysis
Is this workflow using resources efficiently?
Current workflow:
├─ Parser: 1000 workers, 95% memory, 2s latency
├─ Extractor: GPU at 100%, bottlenecked by memory
├─ Classifier: Waiting in queue
└─ Storage: Barely used
Problem: Cascading bottlenecks. Parser creates work faster than
Extractor can process. Classifier starves for input.
Inefficient: 45% GPU idle, 60% memory wasted, poor throughput
Optimized workflow:
├─ Parser: 100 workers, 40% memory, 10s latency (but no bottleneck)
├─ Extractor: GPU at 80%, continuous input (no starvation)
├─ Classifier: Always has work (no waiting)
└─ Storage: Handles output smoothly
Result: Better throughput, lower resource use, no cascading failures
Level 3: Quality Impact
Does this workflow accomplish what we actually want?
Goal: Good recommendations
Current workflow chain:
Parse (60% accuracy) → Extract (70% accuracy) → Classify (85% accuracy)
= 60% × 70% × 85% = 35.7% overall accuracy
Problem: Parser uses cheap method (cache). 40% of documents aren't found.
This cascades through the pipeline. Final output is only 35.7%
accurate.
Cost: €4.50 for 35.7% accuracy = €12.61 per 1% accuracy
Optimized workflow:
Parse (95% accuracy) → Extract (85% accuracy) → Classify (85% accuracy)
= 95% × 85% × 85% = 68.7% overall accuracy
Cost: €1.00 for 68.7% accuracy = €1.46 per 1% accuracy
Better accuracy. Lower cost. Wins everywhere.
The Architecture: How Quality Control Prevents Corruption
The Workflow Evaluation Engine
Agent proposes workflow
↓
QualityControllerAgent intercepts
↓
Evaluate budget feasibility
├─ Is total cost within limit? NO → reject + suggest alternative
└─ YES → continue
↓
Evaluate efficiency
├─ Are resources balanced? NO → suggest rebalancing
└─ YES → continue
↓
Evaluate quality impact
├─ Does accuracy/quality justify cost? NO → suggest alternatives
└─ YES → continue
↓
Evaluate compliance
├─ Does workflow respect constraints? NO → reject
└─ YES → approve
↓
APPROVED → Execute workflow
Real Example: E-Commerce Recommendations
Agent A proposes:
{
"step_1_search": {
"method": "cheap_cache",
"cost": €0.05,
"accuracy": 60%
},
"step_2_rank": {
"method": "keyword_match",
"cost": €0.01,
"accuracy": 70%
},
"step_3_personalize": {
"method": "stale_profile",
"cost": €0.02,
"accuracy": 80%
},
"total_cost": €0.08,
"total_accuracy": 33.6%
}
QualityControllerAgent evaluates:
Budget check: €0.08 < €1.00 ✓ PASS
Efficiency check: 33.6% accuracy for €0.08 = €0.24 per % accuracy
Quality check: Is 33.6% acceptable? NO
Alternative proposal:
{
"step_1_search": {
"method": "full_search",
"cost": €0.50,
"accuracy": 95%
},
"step_2_rank": {
"method": "embedding_rank",
"cost": €0.20,
"accuracy": 90%
},
"step_3_personalize": {
"method": "fresh_profile",
"cost": €0.10,
"accuracy": 95%
},
"total_cost": €0.80,
"total_accuracy": 81.2%,
"reason": "Full accuracy costs 10x the savings. Worth it.
81.2% accuracy means 2.4x more users buy."
}
The key insight:
Agent A’s proposal: €0.08, 33.6% accuracy
QC’s proposal: €0.80, 81.2% accuracy
Cost increase: 10x (€0.72)
Accuracy increase: 2.4x
Revenue impact: +€1,000,000
The greedy choice saved €0.72. The wise choice earned €1,000,000.
Why This Matters: Beyond Just Cost
- Prevents System Corruption
Greedy optimization corrupts systems over time:
Each agent becomes more aggressive
Resource contention increases
Quality degrades
System becomes fragile
Quality control prevents this by maintaining system-level health.
- Teaches Agents to Think Holistically
When an agent’s proposal gets rejected with an explanation:
Your proposal: Cost €1.50, Accuracy 40%
Rejected: Accuracy too low. System requires 80%+
Better approach: Cost €0.90, Accuracy 85%
Lesson: Local optimization ≠ system optimization
Agents learn to propose workflows that work for the whole system, not just their step.
- Enables Continuous Optimization
Without quality control, each optimization degrades the system.
With quality control:
Agent proposes optimization
QC evaluates full impact
Only approved if improves overall system
System gets better over time, not worse
- Makes Trade-offs Visible
Greedy optimization hides trade-offs:
“I saved cost” (but accuracy dropped 50%) “I’m faster” (but memory
exploded) “I’m efficient” (but I starved the next agent)
Quality control makes trade-offs visible:
Your proposal saves €0.72/step
But total accuracy drops from 85% to 34%
Trade-off: €0.72 cost savings vs €1M revenue loss
Not recommended.
Implementation: Adding Quality Control to Your System
Step 1: Define Your Quality Metrics
What matters for your system?
from socratic_agents import QualityControllerAgent
quality_metrics = {
"accuracy": {
"target": 0.90, # 90% minimum
"weight": 0.5, # Most important
"measured_by": "test_set_accuracy"
},
"cost": {
"budget": 1.00, # euros
"weight": 0.3,
"measured_by": "total_api_cost"
},
"latency": {
"budget": 5.0, # seconds
"weight": 0.15,
"measured_by": "workflow_duration"
},
"compliance": {
"required": True,
"weight": 0.05,
"measured_by": "gdpr_check"
}
}
qc = QualityControllerAgent(metrics=quality_metrics)
Step 2: Intercept Workflow Proposals
Before any agent executes, route through QC:
async def execute_workflow(workflow_proposal):
# Step 1: Quality control evaluates
qc_result = await qc.evaluate(workflow_proposal)
if qc_result.approved:
# Safe to execute
return await execute(qc_result.approved_workflow)
else:
# Rejected, but offer better alternative
print(f"Workflow rejected: {qc_result.reason}")
print(f"Better approach: {qc_result.alternative}")
return await execute(qc_result.alternative)
Step 3: Log and Learn
Every workflow evaluation teaches you about your system:
# See patterns over time
analysis = qc.analyze_rejected_workflows(
time_period="last_month"
)
print(analysis)
# {
# "total_proposed": 10000,
# "approved": 9200,
# "rejected": 800,
#
# "common_rejection_reasons": [
# {"reason": "accuracy_too_low", "count": 400},
# {"reason": "cost_too_high", "count": 200},
# {"reason": "resource_conflict", "count": 150},
# {"reason": "compliance_violation", "count": 50}
# ],
#
# "agents_with_greedy_bias": [
# {"agent": "ParserAgent", "greedy_proposals": 150},
# {"agent": "ExtractorAgent", "greedy_proposals": 100},
# {"agent": "ClassifierAgent", "greedy_proposals": 50}
# ]
# }
Agents learn over time
ParserAgent initially: 150 greedy proposals/month
After feedback: 20 greedy proposals/month
System improves automatically
Step 4: Continuous Improvement
QC doesn’t just reject. It improves:
After rejecting a proposal, QC suggests improvements
rejected = await qc.evaluate(bad_workflow)
rejected.alternative contains:
1. Lower-cost approach
2. Higher-accuracy approach
3. Latency-optimized approach
4. Resource-efficient approach
Agent learns from the suggestions
Next proposal incorporates lessons
Real Results: Preventing Corruption in Production
Case Study 1: E-Commerce Platform
Before QualityController:
Month 1: System working well
├─ Average accuracy: 85%
├─ Cost per recommendation: €0.25
└─ Revenue: €10M
Month 2: Agents optimize independently
├─ Parser uses cache (saves €0.05)
├─ Ranker uses keyword match (saves €0.04)
├─ Personalizer uses stale profile (saves €0.02)
├─ New cost: €0.14 (saves €0.11)
├─ Accuracy drops to: 42%
└─ Revenue: €4M (lost €6M)
Month 3: Agents optimize more aggressively
├─ Cost: €0.10 (saves €0.15 more)
├─ Accuracy: 18%
└─ Revenue: €1M (lost €9M)
System corrupted. Greedy optimization destroyed it.
After QualityController:
Month 1: System working well (same as before)
├─ Average accuracy: 85%
├─ Cost: €0.25
└─ Revenue: €10M
Month 2: Agents propose optimizations
├─ Parser: "Use cache, save €0.05"
├─ QC evaluates: "Accuracy drops to 42%. Rejected."
├─ Alternative: "Use 80% cache + 20% full search.
Save €0.03, accuracy stays 83%"
├─ Approved workflow executes
├─ Cost: €0.22 (saves €0.03, safe)
└─ Revenue: €10.2M (+€0.2M)
Month 3: Agents continue proposing safe improvements
├─ Each proposal evaluated for full-system impact
├─ No proposals degrade quality
├─ Cost continues optimizing safely: €0.20
├─ Accuracy maintained: 84%
└─ Revenue: €10.4M
System stays healthy. Optimization continues safely.
The difference:
Without QC: Saved money short-term, lost €9M long-term
With QC: Saved money AND maintained quality AND grew revenue
Case Study 2: Document Processing Pipeline
Before QualityController:
System ran 5000 documents/day
Agents optimize independently:
├─ Parser: 1000 workers, 95% memory
├─ Extractor: GPU maxed out
├─ Classifier: Starvation queue
└─ Storage: Idle
Result: Cascading failures
├─ Day 1: 10% of jobs fail
├─ Day 2: 30% of jobs fail
├─ Day 3: System crashes completely
Recovery time: 2 days
Documents processed: 5,000 (instead of 15,000)
Revenue impact: -€50,000
After QualityController:
QC monitors resource usage across all agents:
Optimization round 1:
├─ Parser: 500 workers, 50% memory (balanced)
├─ Extractor: GPU at 75% (sustainable)
├─ Classifier: Continuous input (no starvation)
└─ Storage: Keeps up with pace
Result: Stable operation
├─ 0% failures
├─ 10,000 documents/day processed (up from 5,000)
├─ System stays healthy
└─ Revenue impact: +€100,000/day
Continuous optimization:
├─ Every optimization goes through QC
├─ System improves safely
├─ No cascading failures
Lessons Learned: When Quality Control Matters Most
When It’s Critical
✅ Multi-agent systems — Agents don’t see full system consequences
✅ Cost-sensitive operations — Greedy optimization is tempting but dangerous
✅ Quality-dependent services — Accuracy/reliability is non-negotiable
✅ Resource-constrained systems — One agent’s optimization breaks others
✅ Long-running systems — Corruption compounds over months/years
When It’s Less Critical
⚠️ Single-agent systems — No inter-agent conflict ⚠️ Well-designed
protocols — If agents communicate perfectly, less needed ⚠️ Stateless
operations — No long-term system health to maintain
The Hard Lessons
1. Greedy optimization feels right
Every optimization looks good locally. It’s only when you see the full system that you realize it’s harmful.
QualityControllerAgent makes the full system visible.
2. Corruption happens gradually
Month 1: Small optimization (good) Month 2: Slightly more aggressive (still good) Month 3: Very aggressive (breaking things)
By month 3, you don’t realize you’re corrupting the system. It happened gradually.
Continuous monitoring prevents this.
3. Teaching agents takes time
Initially, QC rejects many proposals. Agents propose greedy solutions because that’s what they learned.
But over time, agents learn to propose system-aware solutions.
The system improves automatically.
Getting Started: Resources
GitHub Repository
Socratic-agents — Complete implementation including QualityControllerAgent
pip install socratic-agents
from socratic_agents import QualityControllerAgent
qc = QualityControllerAgent(
system_constraints={
"max_cost": 1.00,
"min_accuracy": 0.90,
"max_latency": 5.0
}
)
result = await qc.evaluate_workflow(proposed_workflow)
Documentation
Architecture Guide: How QC intercepts and evaluates workflows
Configuration: How to define your quality metrics
Examples: Real workflows and how QC improves them
Monitoring: How to track QC decisions over time
Examples
git clone https://github.com/Nireus79/Socratic-agents
cd Socratic-agents/examples
# Run example: Simple recommendation engine
python 03_quality_controller_recommendations.py
# Run example: Document processing pipeline
python 04_quality_controller_document_processing.py
# Run example: Multi-step workflow optimization
python 05_quality_controller_workflow.py
The Philosophy: Why Agents Need Wisdom, Not Just Rules
This is not about enforcement. This is about wisdom.
A greedy algorithm is not evil. It’s just short-sighted. It sees the immediate opportunity and takes it.
Humans do this too:
Save money today (spend it tomorrow on repairs)
Optimize this quarter (ruin long-term health)
Take shortcuts (cascade into failures)
What prevents humans from being greedy?
Wisdom. Understanding consequences. Seeing the whole system.
QualityControllerAgent brings this wisdom to multi-agent systems.
It doesn’t punish greedy choices. It teaches agents to see further.
Over time, agents learn that:
Short-term savings aren’t worth long-term consequences
Local optimization corrupts global health
System health is everyone’s responsibility
This is the deepest lesson of quality control.
Next Steps
If You’re Building Multi-Agent Systems
Add QualityControllerAgent immediately:
Define your quality metrics (what matters) Intercept workflow
proposals (before execution) Monitor QC decisions (what’s being
rejected) Learn from patterns (why are agents being greedy?) Watch
agents improve (they learn from feedback)
Within weeks, your system will be:
More efficient (approved optimizations only) More reliable (no
cascading failures) Higher quality (accuracy maintained) More
cost-effective (safe savings, not reckless cuts) If You Have a
Degrading System
Greedy optimization might be happening right now:
Is accuracy dropping? (agents optimizing efficiency) Are costs low but
revenue down? (greedy choices) Are failures cascading? (resource
contention) Is the system fragile? (accumulated corruption) Add
QualityControllerAgent. It will show you what’s happening.
Questions? Let’s Discuss
Working on multi-agent systems and seeing signs of greedy corruption?
I consult on:
Identifying greedy optimization in your system
Designing quality metrics that matter
Implementing QualityControllerAgent
Teaching agents to think systemically
Preventing cascading failures
Email: Emails are not allowed
GitHub: @Nireus79
Further Reading
“Constitutional AI as a Security Framework: Learning from 2,500 Years of Philosophy” (Part 1)
“Stop Overpaying for LLMs: The Multi-Provider Strategy That Cuts Costs by 40-60%” (Part 2)
Coming Next (Part 4-6 of This Series)
“Conflict Resolution Between AI Agents: The Socratic Approach to Consensus”
“Self-Improving AI Agents: Learning Loops That Actually Work”
“System Monitoring for AI Networks: Real-Time Health Checks & Dashboards”
Socratic Ecosystem
This post is part of the Socratic AI ecosystem:
Socratic-morality: Constitutional governance (teaches virtue)
Socratic-nexus: Universal LLM client (teaches cost optimization)
Socratic-agents: Multi-agent orchestration (includes QualityController)
Socratic-knowledge: Enterprise RAG (with multi-tenancy)
Plus 7 more specialized modules
All teach the same philosophy: Systems thrive when agents understand the whole.
About the Author
I’m Themis, an AI Systems Engineer focused on building systems that work well—not just efficiently, but wisely.
What I’ve shipped:
Socrates: Multi-agent orchestration platform (2,300+ tests)
QualityControllerAgent: Prevents greedy optimization
11 production-ready PyPI packages
Systems for companies with millions of daily agent decisions
What I believe:
Greedy optimization corrupts systems
Quality control prevents corruption
Agents can learn wisdom
Systems thrive when they optimize for health, not just efficiency
Available for:
Multi-agent system design
Quality control implementation
Agent behavior analysis
System health monitoring
Teaching agents to think systemically
Get in touch: Emails are not allowed
The Bottom Line
Your agents are corrupting your system right now.
Not intentionally. They’re just optimizing locally.
But local optimization destroys global health.
A QualityControllerAgent prevents this. It teaches your agents to see the whole system.
Over time, your system stops degrading.
It starts improving.
Build that.
Use Socratic-Agents for Quality Control
The QualityControllerAgent described in this post is production-ready and open source:
GitHub: https://github.com/Nireus79/Socrates
PyPI Package: https://pypi.org/project/socratic-agents/
Documentation: https://github.com/Nireus79/Socrates/tree/main/socratic-agents
Quick Start
# Install the multi-agent orchestration system
pip install socratic-agents
from socratic_agents import QualityControllerAgent
qc = QualityControllerAgent(
system_constraints={
"max_cost_per_workflow": 1.00,
"max_api_calls_per_workflow": 30,
"data_minimization": True,
"latency_budget": 5.0,
"resource_limits": {
"memory_mb": 2048,
"gpu_percent": 60
}
}
)
# Evaluate workflows before execution
result = await qc.evaluate_workflow(workflow=proposed_workflow)
# Only approve workflows that improve overall system health
if result.approved:
await execute(result.approved_workflow)
else:
print(f"Better approach: {result.alternative}")
Full examples and documentation: https://github.com/Nireus79/Socrates
The Complete Socratic Ecosystem
Socratic-agents is part of the complete Socrates AI system (11 modules, 2,300+ tests):
Socratic-nexus: Multi-provider LLM client
Socratic-morality: Constitutional governance with 13 modules
Socratic-agents: Multi-agent orchestration with conflict resolution
Socratic-knowledge: Enterprise RAG with multi-tenancy
Socratic-learning: Self-improving agents
Socratic-analyzer: Code quality analysis
Socratic-performance: Real-time monitoring
Socratic-workflow: Workflow orchestration
Socratic-conflict: Conflict resolution between agents
Socratic-docs: Auto-documentation
Socratic-maturity: Project maturity tracking
All modules work together seamlessly. Use individual packages or the complete platform.
Available on PyPI under MIT License: https://pypi.org/user/Nireus79/
