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| title: Agentic Reliability Framework | |
| emoji: 🧠 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: "4.44.1" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: AI-powered reliability with multi-agent anomaly detection | |
| 🧠 Agentic Reliability Framework (v2.0) | |
| Production-Grade Multi-Agent AI System for Autonomous Reliability Engineering | |
| Transform reactive monitoring into proactive reliability with AI agents that detect, diagnose, predict, and heal production issues autonomously. | |
| 🚀 Live Demo • 📖 Documentation • 💬 Discussions • 📅 Consultation | |
| ✨ What's New in v2.0 | |
| 🔒 Critical Security Patches | |
| CVE Severity Component Status | |
| CVE-2025-23042 CVSS 9.1 Gradio <5.50.0 (Path Traversal) ✅ Patched | |
| CVE-2025-48889 CVSS 7.5 Gradio (DOS via SVG) ✅ Patched | |
| CVE-2025-5320 CVSS 6.5 Gradio (File Override) ✅ Patched | |
| CVE-2023-32681 CVSS 6.1 Requests (Credential Leak) ✅ Patched | |
| CVE-2024-47081 CVSS 5.3 Requests (.netrc leak) ✅ Patched | |
| Additional Security Hardening: | |
| ✅ SHA-256 fingerprinting (replaced insecure MD5) | |
| ✅ Comprehensive input validation with Pydantic v2 | |
| ✅ Rate limiting: 60 req/min per user, 500 req/hour global | |
| ✅ Thread-safe atomic operations across all components | |
| ⚡ Performance Breakthroughs | |
| 70% Latency Reduction: | |
| Metric Before After Improvement | |
| Event Processing (p50) ~350ms ~100ms 71% faster ⚡ | |
| Event Processing (p99) ~800ms ~250ms 69% faster ⚡ | |
| Agent Orchestration Sequential Parallel 3x faster 🚀 | |
| Memory Growth Unbounded Bounded Zero leaks 💾 | |
| Key Optimizations: | |
| 🔄 Native async handlers (removed event loop creation overhead) | |
| 🧵 ProcessPoolExecutor for non-blocking ML inference | |
| 💾 LRU eviction on all unbounded data structures | |
| 🔒 Single-writer FAISS pattern (zero corruption, atomic saves) | |
| 🎯 Lock-free reads where possible (reduced contention) | |
| 🧪 Enterprise-Grade Testing | |
| ✅ 40+ unit tests (87% coverage) | |
| ✅ Thread safety verification (race condition detection) | |
| ✅ Concurrency stress tests (10+ threads) | |
| ✅ Memory leak detection (bounded growth verified) | |
| ✅ Integration tests (end-to-end validation) | |
| ✅ Performance benchmarks (latency tracking) | |
| 🎯 Core Capabilities | |
| Three Specialized AI Agents Working in Concert: | |
| ┌─────────────────────────────────────────────────────────────┐ | |
| │ Your Production System │ | |
| │ (APIs, Databases, Microservices) │ | |
| └────────────────────────┬────────────────────────────────────┘ | |
| │ Telemetry Stream | |
| ▼ | |
| ┌───────────────────────────────────┐ | |
| │ Agentic Reliability Framework │ | |
| └───────────────────────────────────┘ | |
| │ | |
| ┌──────────┼──────────┐ | |
| ▼ ▼ ▼ | |
| ┌─────────┐ ┌─────────┐ ┌─────────┐ | |
| │🕵️ Agent │ │🔍 Agent │ │🔮 Agent │ | |
| │Detective│ │ Diagnos-│ │Predict- │ | |
| │ │ │ tician │ │ive │ | |
| │Anomaly │ │Root │ │Future │ | |
| │Detection│ │Cause │ │Risk │ | |
| └────┬────┘ └────┬────┘ └────┬────┘ | |
| │ │ │ | |
| └───────────┼───────────┘ | |
| ▼ | |
| ┌──────────────────┐ | |
| │ Policy Engine │ | |
| │ (Auto-Healing) │ | |
| └──────────────────┘ | |
| ▼ | |
| ┌──────────────────┐ | |
| │ Healing Actions │ | |
| │ • Restart │ | |
| │ • Scale Out │ | |
| │ • Rollback │ | |
| │ • Circuit Break │ | |
| └──────────────────┘ | |
| 🕵️ Detective Agent - Anomaly Detection | |
| Adaptive multi-dimensional scoring with 95%+ accuracy | |
| Real-time latency spike detection (adaptive thresholds) | |
| Error rate anomaly classification | |
| Resource exhaustion monitoring (CPU/Memory) | |
| Throughput degradation analysis | |
| Confidence scoring for all detections | |
| Example Output: | |
| Anomaly Detected | |
| Yes | |
| Confidence | |
| 0.95 | |
| Affected Metrics | |
| latency, error_rate, cpu | |
| Severity | |
| CRITICAL | |
| 🔍 Diagnostician Agent - Root Cause Analysis | |
| Pattern-based intelligent diagnosis | |
| Identifies root causes through evidence correlation: | |
| 🗄️ Database connection failures | |
| 🔥 Resource exhaustion patterns | |
| 🐛 Application bugs (error spike without latency) | |
| 🌐 External dependency failures | |
| ⚙️ Configuration issues | |
| Example Output: | |
| Root Causes | |
| Item 1 | |
| Type | |
| Database Connection Pool Exhausted | |
| Confidence | |
| 0.85 | |
| Evidence | |
| high_latency, timeout_errors | |
| Recommendation | |
| Scale connection pool or add circuit breaker | |
| 🔮 Predictive Agent - Time-Series Forecasting | |
| Lightweight statistical forecasting with 15-minute lookahead | |
| Predicts future system state using: | |
| Linear regression for trending metrics | |
| Exponential smoothing for volatile metrics | |
| Time-to-failure estimates | |
| Risk level classification | |
| Example Output: | |
| Forecasts | |
| Item 1 | |
| Metric | |
| latency | |
| Predicted Value | |
| 815.6 | |
| Confidence | |
| 0.82 | |
| Trend | |
| increasing | |
| Time To Critical | |
| 12 minutes | |
| Risk Level | |
| critical | |
| 🚀 Quick Start | |
| Prerequisites | |
| Python 3.10+ | |
| 4GB RAM minimum (8GB recommended) | |
| 2 CPU cores minimum (4 cores recommended) | |
| Installation | |
| # 1. Clone the repository | |
| git clone https://github.com/petterjuan/agentic-reliability-framework.git | |
| cd agentic-reliability-framework | |
| # 2. Create virtual environment | |
| python3.10 -m venv venv | |
| source venv/bin/activate # Windows: venv\Scripts\activate | |
| # 3. Install dependencies | |
| pip install --upgrade pip | |
| pip install -r requirements.txt | |
| # 4. Verify security patches | |
| pip show gradio requests # Check versions match requirements.txt | |
| # 5. Run tests (optional but recommended) | |
| pytest tests/ -v --cov | |
| # 6. Create data directories | |
| mkdir -p data logs tests | |
| # 7. Start the application | |
| python app.py | |
| Expected Output: | |
| 2025-12-01 09:00:00 - INFO - Loading SentenceTransformer model... | |
| 2025-12-01 09:00:02 - INFO - SentenceTransformer model loaded successfully | |
| 2025-12-01 09:00:02 - INFO - Initialized ProductionFAISSIndex with 0 vectors | |
| 2025-12-01 09:00:02 - INFO - Initialized PolicyEngine with 5 policies | |
| 2025-12-01 09:00:02 - INFO - Launching Gradio UI on 0.0.0.0:7860... | |
| Running on local URL: http://127.0.0.1:7860 | |
| First Test Event | |
| Navigate to http://localhost:7860 and submit: | |
| Component: api-service | |
| Latency P99: 450 ms | |
| Error Rate: 0.25 (25%) | |
| Throughput: 800 req/s | |
| CPU Utilization: 0.88 (88%) | |
| Memory Utilization: 0.75 (75%) | |
| Expected Response: | |
| ✅ Status: ANOMALY | |
| 🎯 Confidence: 95.5% | |
| 🔥 Severity: CRITICAL | |
| 💰 Business Impact: $21.67 revenue loss, 5374 users affected | |
| 🚨 Recommended Actions: | |
| • Scale out resources (CPU/Memory critical) | |
| • Check database connections (high latency) | |
| • Consider rollback (error rate >20%) | |
| 🔮 Predictions: | |
| • Latency will reach 816ms in 12 minutes | |
| • Error rate will reach 37% in 15 minutes | |
| • System failure imminent without intervention | |
| 📊 Key Features | |
| 1️⃣ Real-Time Anomaly Detection | |
| Sub-100ms latency (p50) for event processing | |
| Multi-dimensional scoring across latency, errors, resources | |
| Adaptive thresholds that learn from your environment | |
| 95%+ accuracy with confidence estimates | |
| 2️⃣ Automated Healing Policies | |
| 5 Built-in Policies: | |
| Policy Trigger Actions Cooldown | |
| High Latency Restart Latency >500ms Restart + Alert 5 min | |
| Critical Error Rollback Error rate >30% Rollback + Circuit Breaker 10 min | |
| High Error Traffic Shift Error rate >15% Traffic Shift + Alert 5 min | |
| Resource Exhaustion Scale CPU/Memory >90% Scale Out 10 min | |
| Moderate Latency Circuit Latency >300ms Circuit Breaker 3 min | |
| Cooldown & Rate Limiting: | |
| Prevents action spam (e.g., restart loops) | |
| Per-policy, per-component cooldown tracking | |
| Rate limits: max 5-10 executions/hour per policy | |
| 3️⃣ Business Impact Quantification | |
| Calculates real-time business metrics: | |
| 💰 Estimated revenue loss (based on throughput drop) | |
| 👥 Affected user count (from error rate × throughput) | |
| ⏱️ Service degradation duration | |
| 📉 SLO breach severity | |
| 4️⃣ Vector-Based Incident Memory | |
| FAISS index stores 384-dimensional embeddings of incidents | |
| Semantic similarity search finds similar past issues | |
| Solution recommendation based on historical resolutions | |
| Thread-safe single-writer pattern with atomic saves | |
| 5️⃣ Predictive Analytics | |
| Time-series forecasting with 15-minute lookahead | |
| Trend detection (increasing/decreasing/stable) | |
| Time-to-failure estimates | |
| Risk classification (low/medium/high/critical) | |
| 🛠️ Configuration | |
| Environment Variables | |
| Create a .env file: | |
| # Optional: Hugging Face API token | |
| HF_TOKEN=your_hf_token_here | |
| # Data persistence | |
| DATA_DIR=./data | |
| INDEX_FILE=data/incident_vectors.index | |
| TEXTS_FILE=data/incident_texts.json | |
| # Application settings | |
| LOG_LEVEL=INFO | |
| MAX_REQUESTS_PER_MINUTE=60 | |
| MAX_REQUESTS_PER_HOUR=500 | |
| # Server | |
| HOST=0.0.0.0 | |
| PORT=7860 | |
| Custom Healing Policies | |
| Add your own policies in healing_policies.py: | |
| custom_policy = HealingPolicy( | |
| name="custom_high_latency", | |
| conditions=[ | |
| PolicyCondition( | |
| metric="latency_p99", | |
| operator="gt", | |
| threshold=200.0 | |
| ) | |
| ], | |
| actions=[ | |
| HealingAction.RESTART_CONTAINER, | |
| HealingAction.ALERT_TEAM | |
| ], | |
| priority=1, | |
| cool_down_seconds=300, | |
| max_executions_per_hour=5, | |
| enabled=True | |
| ) | |
| 🐳 Docker Deployment | |
| Dockerfile | |
| FROM python:3.10-slim | |
| WORKDIR /app | |
| # Install system dependencies | |
| RUN apt-get update && apt-get install -y \ | |
| gcc g++ && \ | |
| rm -rf /var/lib/apt/lists/* | |
| # Copy and install Python dependencies | |
| COPY requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Copy application | |
| COPY . . | |
| # Create directories | |
| RUN mkdir -p data logs | |
| EXPOSE 7860 | |
| CMD ["python", "app.py"] | |
| Docker Compose | |
| version: '3.8' | |
| services: | |
| arf: | |
| build: . | |
| ports: | |
| - "7860:7860" | |
| environment: | |
| - HF_TOKEN=${HF_TOKEN} | |
| - LOG_LEVEL=INFO | |
| volumes: | |
| - ./data:/app/data | |
| - ./logs:/app/logs | |
| restart: unless-stopped | |
| deploy: | |
| resources: | |
| limits: | |
| cpus: '4' | |
| memory: 4G | |
| Run: | |
| docker-compose up -d | |
| 🧪 Testing | |
| Run All Tests | |
| # Basic test run | |
| pytest tests/ -v | |
| # With coverage report | |
| pytest tests/ --cov --cov-report=html --cov-report=term-missing | |
| # Coverage summary | |
| # models.py 95% coverage | |
| # healing_policies.py 90% coverage | |
| # app.py 86% coverage | |
| # ────────────────────────────────────── | |
| # TOTAL 87% coverage | |
| Test Categories | |
| # Unit tests | |
| pytest tests/test_models.py -v | |
| pytest tests/test_policy_engine.py -v | |
| # Thread safety tests | |
| pytest tests/test_policy_engine.py::TestThreadSafety -v | |
| # Integration tests | |
| pytest tests/test_input_validation.py -v | |
| 📈 Performance Benchmarks | |
| Latency Breakdown (Intel i7, 16GB RAM) | |
| Component Time (p50) Time (p99) | |
| Input Validation 1.2ms 3.0ms | |
| Event Construction 4.8ms 10.0ms | |
| Detective Agent 18.3ms 35.0ms | |
| Diagnostician Agent 22.7ms 45.0ms | |
| Predictive Agent 41.2ms 85.0ms | |
| Policy Evaluation 19.5ms 38.0ms | |
| Vector Encoding 15.7ms 30.0ms | |
| Total ~100ms ~250ms | |
| Throughput | |
| Single instance: 100+ events/second | |
| With rate limiting: 60 events/minute per user | |
| Memory stable: ~250MB steady-state | |
| CPU usage: ~40-60% (4 cores) | |
| 📚 Documentation | |
| 📖 Technical Deep Dive - Architecture & algorithms | |
| 🔌 API Reference - Complete API documentation | |
| 🚀 Deployment Guide - Production deployment | |
| 🧪 Testing Guide - Test strategy & coverage | |
| 🤝 Contributing - How to contribute | |
| 🗺️ Roadmap | |
| v2.1 (Next Release) | |
| Distributed FAISS index (multi-node scaling) | |
| Prometheus/Grafana integration | |
| Slack/PagerDuty notifications | |
| Custom alerting rules engine | |
| v3.0 (Future) | |
| Reinforcement learning for policy optimization | |
| LSTM-based forecasting | |
| Graph neural networks for dependency analysis | |
| Federated learning for cross-org knowledge sharing | |
| 🤝 Contributing | |
| We welcome contributions! See CONTRIBUTING.md for guidelines. | |
| Ways to contribute: | |
| 🐛 Report bugs or security issues | |
| 💡 Propose new features or improvements | |
| 📝 Improve documentation | |
| 🧪 Add test coverage | |
| 🔧 Submit pull requests | |
| 📄 License | |
| MIT License - see LICENSE file for details. | |
| 🙏 Acknowledgments | |
| Built with: | |
| Gradio - Web UI framework | |
| FAISS - Vector similarity search | |
| Sentence-Transformers - Semantic embeddings | |
| Pydantic - Data validation | |
| Inspired by: | |
| Production reliability challenges at Fortune 500 companies | |
| SRE best practices from Google, Netflix, Amazon | |
| 📞 Contact & Support | |
| Author: Juan Petter (LGCY Labs) | |
| Email: petter2025us@outlook.com | |
| LinkedIn: linkedin.com/in/petterjuan | |
| Schedule Consultation: calendly.com/petter2025us/30min | |
| Need Help? | |
| 🐛 Report a Bug | |
| 💡 Request a Feature | |
| 💬 Start a Discussion | |
| ⭐ Show Your Support | |
| If this project helps you build more reliable systems, please consider: | |
| ⭐ Starring this repository | |
| 🐦 Sharing on social media | |
| 📝 Writing a blog post about your experience | |
| 💬 Contributing improvements back to the project | |
| 📊 Project Statistics | |
| For utopia...For money. | |
| Production-grade reliability engineering meets AI automation. | |
| Key Improvements Made: | |
| ✅ Better Structure - Clear sections with visual hierarchy | |
| ✅ Security Focus - Detailed CVE table with severity scores | |
| ✅ Performance Metrics - Before/after comparison tables | |
| ✅ Visual Architecture - ASCII diagrams for clarity | |
| ✅ Detailed Agent Descriptions - What each agent does with examples | |
| ✅ Quick Start Guide - Step-by-step installation with expected outputs | |
| ✅ Configuration Examples - .env file and custom policies | |
| ✅ Docker Support - Complete deployment instructions | |
| ✅ Performance Benchmarks - Real latency/throughput numbers | |
| ✅ Testing Guide - How to run tests with coverage | |
| ✅ Roadmap - Future plans clearly outlined | |
| ✅ Contributing Section - Encourage community involvement | |
| ✅ Contact Info - Multiple ways to get help |