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⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.

Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense

🚀 Why FailureForge?

Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?

FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.

Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.

✨ Highlights

  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard

📌 Features

FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅

🏗 Architecture

 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.

⚙ Tech Stack

Backend

  • Python
  • FastAPI

Frontend

  • Next.js 15
  • TypeScript
  • Tailwind CSS

Infrastructure

  • Docker
  • Docker Compose

Database

  • PostgreSQL
  • Redis

AI

  • Gemini API

Testing

  • Pytest
  • Playwright

📂 Repository Structure

backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start

Clone Repository

git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge

Start Everything

make up

Open

http://localhost:3000

🚨 Run Failure Scenarios

Redis Outage

make redis-outage

Database Deadlock

make deadlock

Memory Leak

make memory-leak

Slow Database

make slow-database

List captured incidents

make incidents

🤖 AI Diagnosis

FailureForge supports two diagnosis providers.

Gemini

Create a .env

GEMINI_API_KEY=your_api_key

Offline Stub

DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo

Generate benchmark metrics

make evaluation

🔄 Demo Workflow

  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.

🧪 Testing

make test

Backend End-to-End

make e2e

Frontend Playwright

make e2e-ui

📈 Future Roadmap

Planned features include:

  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations

🎯 Motivation

Current AI benchmarks evaluate language understanding.

FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.

It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.

🤝 Contributing

Contributions, ideas, and feature requests are always welcome.

Please check CONTRIBUTING.md before submitting a pull request.

⭐ If you found this project useful

Please consider giving the repository a Star ⭐.

About

No description, website, or topics provided.

Resources

Readme
MIT license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

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GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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, 'i');
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try {
var __m = "*";
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GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
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});
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unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
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html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
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[data-theme="light"], [data-theme="light"],
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.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
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pre code, pre code * {
filter: none !important;
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background: #2d2d44 !important;
border-color: #444 !important;
}
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
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::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
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if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
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applyDarkMode();
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removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
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// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
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observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - Zoro-1012/failureforge · GitHub
Skip to content
BranchesTags
Repository files navigation
⚒️ FailureForge

An AI-powered distributed systems failure simulation and LLM evaluation platform.


Generate infrastructure failures • Capture real telemetry • Diagnose with AI • Benchmark against Ground Truth

PythonFastAPINext.jsDockerPostgreSQLRedisGeminiLicense
🚀 Why FailureForge?
Modern LLMs can summarize logs, but can they accurately identify the real root cause of infrastructure failures?
FailureForge creates controlled failures inside a distributed environment, captures production-like telemetry, diagnoses incidents using AI, and evaluates the diagnosis against the known ground truth.
Because every incident is intentionally injected, the true root cause is always known—making diagnostic benchmarking objective and reproducible.
✨ Highlights
  • ⚡ Docker-based distributed environment
  • 🤖 AI-powered root cause analysis
  • 📊 Automatic benchmark generation
  • 📈 Precision / Recall / F1 evaluation
  • 🔬 Reproducible experiments
  • 📦 Exportable benchmark datasets
  • 🧪 End-to-end tested
  • 🌐 Interactive Next.js dashboard
📌 Features
FeatureStatus
Redis Outage Simulation✅
PostgreSQL Deadlock Simulation✅
Memory Leak Simulation✅
Slow Database Simulation✅
Telemetry Collection✅
Incident Persistence✅
AI Diagnosis Engine✅
Evaluation Framework✅
Interactive Dashboard✅
🏗 Architecture
 User
│
▼
Next.js Dashboard
│
▼
FastAPI Backend / Orchestrator
│
▼
Failure Orchestrator
│
▼
Distributed Environment
(App + PostgreSQL + Redis)
│
▼
Telemetry Collection
│
▼
PostgreSQL
│
▼
AI Diagnosis Engine
│
▼
Evaluation Engine

A complete architecture diagram with Mermaid visualizations is available in ARCHITECTURE.md.
⚙ Tech Stack
Backend
  • Python
  • FastAPI
Frontend
  • Next.js 15
  • TypeScript
  • Tailwind CSS
Infrastructure
  • Docker
  • Docker Compose
Database
  • PostgreSQL
  • Redis
AI
  • Gemini API
Testing
  • Pytest
  • Playwright
📂 Repository Structure
backend/
├── app/
├── orchestrator/
frontend/
docker/
datasets/
scripts/
docs/
tests/

🚀 Quick Start
Clone Repository
git clone https://github.com/Zoro-1012/failureforge.git
cd failureforge
Start Everything
make up
Open
http://localhost:3000

🚨 Run Failure Scenarios
Redis Outage
make redis-outage
Database Deadlock
make deadlock
Memory Leak
make memory-leak
Slow Database
make slow-database
List captured incidents
make incidents
🤖 AI Diagnosis
FailureForge supports two diagnosis providers.
Gemini
Create a .env
GEMINI_API_KEY=your_api_key
Offline Stub
DIAGNOSIS_PROVIDER=stub docker compose -f docker/docker-compose.yml up -d --force-recreate orchestrator
make demo
Generate benchmark metrics
make evaluation
🔄 Demo Workflow
  1. Launch a failure scenario.
  2. Infrastructure failure is injected.
  3. Application emits logs and metrics.
  4. Telemetry is captured.
  5. Incident is stored in PostgreSQL.
  6. AI generates root cause analysis.
  7. Evaluation compares prediction with ground truth.
  8. Benchmark metrics are updated.
🧪 Testing
make test
Backend End-to-End
make e2e
Frontend Playwright
make e2e-ui
📈 Future Roadmap
Planned features include:
  • Upload any Docker Compose application
  • Automatic dependency discovery
  • Dynamic fault injection
  • AI-generated resilience reports
  • Reliability scoring
  • Automated remediation recommendations
🎯 Motivation
Current AI benchmarks evaluate language understanding.
FailureForge evaluates whether an AI model can accurately diagnose distributed system failures using real infrastructure telemetry.
It bridges the gap between Reliability Engineering, Chaos Engineering, Observability, and Generative AI.
🤝 Contributing
Contributions, ideas, and feature requests are always welcome.
Please check CONTRIBUTING.md before submitting a pull request.
⭐ If you found this project useful
Please consider giving the repository a Star ⭐.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages