Merge remote changes and resolve README.md conflicts

This commit is contained in:
Francisco M Humarang Jr. 2026-01-05 16:55:44 +08:00
commit be8a87262a
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@ -116,7 +116,6 @@ docs/*
!docs/TEST_SCENARIOS.md
!docs/MODULES.md
!docs/DEVELOPER_FAQ.md
!docs/PUBLISHING.md
!docs/CONTRIBUTING.md
!docs/API_SPECIFICATION.md
!docs/TESTING_GUIDE.md

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README.md
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@ -36,6 +36,7 @@ Instead of running one test case, Flakestorm takes a single "Golden Prompt", gen
> **"If it passes Flakestorm, it won't break in Production."**
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## Who Flakestorm Is For
- **Teams shipping AI agents to production** — Catch failures before users do
@ -51,6 +52,19 @@ Flakestorm is built for production-grade agents handling real traffic. While it
- ✅ **Invariant Assertions**: Deterministic checks, semantic similarity, basic safety
- ✅ **CI/CD Ready**: Run in pipelines with exit codes and score thresholds
- ✅ **Beautiful Reports**: Interactive HTML reports with pass/fail matrices
=======
## What You Get in Minutes
Within minutes of setup, Flakestorm gives you:
- **Robustness Score**: A single number (0.0-1.0) that quantifies your agent's reliability
- **Failure Analysis**: Detailed reports showing exactly which mutations broke your agent and why
- **Security Insights**: Discover prompt injection vulnerabilities before attackers do
- **Edge Case Discovery**: Find boundary conditions that would cause production failures
- **Actionable Reports**: Interactive HTML reports with specific recommendations for improvement
No more guessing if your agent is production-ready. Flakestorm tells you exactly what will break and how to fix it.
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## Demo
@ -74,76 +88,88 @@ Flakestorm is built for production-grade agents handling real traffic. While it
*Interactive HTML reports with detailed failure analysis and recommendations*
## Quick Start
## Try Flakestorm in ~60 Seconds
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> **Note**: This local path is great for quick exploration. Production teams typically run Flakestorm in CI or cloud-based setups. See the [Usage Guide](docs/USAGE_GUIDE.md) for production deployment patterns.
### Local Installation (OSS)
=======
Want to see Flakestorm in action immediately? Here's the fastest path:
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1. **Install Ollama first** (system-level service)
2. **Create virtual environment** (for Python packages)
3. **Install flakestorm** (Python package)
4. **Start Ollama and pull model** (required for mutations)
1. **Install flakestorm** (if you have Python 3.10+):
```bash
pip install flakestorm
```
### Step 1: Install Ollama (System-Level)
2. **Initialize a test configuration**:
```bash
flakestorm init
```
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For local execution, FlakeStorm uses [Ollama](https://ollama.ai) for mutation generation. This is an implementation detail for the OSS path — production setups typically use cloud-based mutation services. Install this first:
=======
3. **Point it at your agent** (edit `flakestorm.yaml`):
```yaml
agent:
endpoint: "http://localhost:8000/invoke" # Your agent's endpoint
type: "http"
```
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**macOS Installation:**
4. **Run your first test**:
```bash
# Option 1: Homebrew (recommended)
brew install ollama
# If you get permission errors, fix permissions first:
sudo chown -R $(whoami) /Users/imac-frank/Library/Logs/Homebrew
sudo chown -R $(whoami) /usr/local/Cellar
sudo chown -R $(whoami) /usr/local/Homebrew
brew install ollama
# Option 2: Official Installer
# Visit https://ollama.ai/download and download the macOS installer (.dmg)
flakestorm run
```
**Windows Installation:**
That's it! You'll get a robustness score and detailed report showing how your agent handles adversarial inputs.
1. Visit https://ollama.com/download/windows
2. Download `OllamaSetup.exe`
3. Run the installer and follow the wizard
4. Ollama will be installed and start automatically
> **Note**: For full local execution (including mutation generation), you'll need Ollama installed. See the [Usage Guide](docs/USAGE_GUIDE.md) for complete setup instructions.
**Linux Installation:**
## How Flakestorm Works
```bash
# Using the official install script
curl -fsSL https://ollama.com/install.sh | sh
Flakestorm follows a simple but powerful workflow:
# Or using package managers (Ubuntu/Debian example):
sudo apt install ollama
```
1. **You provide "Golden Prompts"** — example inputs that should always work correctly
2. **Flakestorm generates mutations** — using a local LLM, it creates adversarial variations:
- Paraphrases (same meaning, different words)
- Typos and noise (realistic user errors)
- Tone shifts (frustrated, urgent, aggressive users)
- Prompt injections (security attacks)
- Encoding attacks (Base64, URL encoding)
- Context manipulation (noisy, verbose inputs)
- Length extremes (empty, very long inputs)
3. **Your agent processes each mutation** — Flakestorm sends them to your agent endpoint
4. **Invariants are checked** — responses are validated against rules you define (latency, content, safety)
5. **Robustness Score is calculated** — weighted by mutation difficulty and importance
6. **Report is generated** — interactive HTML showing what passed, what failed, and why
**After installation, start Ollama and pull the model:**
The result: You know exactly how your agent will behave under stress before users ever see it.
```bash
# Start Ollama
# macOS (Homebrew): brew services start ollama
# macOS (Manual) / Linux: ollama serve
# Windows: Starts automatically as a service
## Features
# In another terminal, pull the model
# Choose based on your RAM:
# - 8GB RAM: ollama pull tinyllama:1.1b or gemma2:2b
# - 16GB RAM: ollama pull qwen2.5:3b (recommended)
# - 32GB+ RAM: ollama pull qwen2.5-coder:7b (best quality)
ollama pull qwen2.5:3b
```
- ✅ **8 Core Mutation Types**: Comprehensive robustness testing covering semantic, input, security, and edge cases
- ✅ **Invariant Assertions**: Deterministic checks, semantic similarity, basic safety
- ✅ **Local-First**: Uses Ollama with Qwen 3 8B for free testing
- ✅ **Beautiful Reports**: Interactive HTML reports with pass/fail matrices
**Troubleshooting:** If you get `syntax error: <!doctype html>` or `command not found` when running `ollama` commands:
## Toward a Zero-Setup Path
```bash
# 1. Remove the bad binary
sudo rm /usr/local/bin/ollama
We're working on making Flakestorm even easier to use. Future improvements include:
- **Cloud-hosted mutation generation**: No need to install Ollama locally
- **One-command setup**: Automated installation and configuration
- **Docker containers**: Pre-configured environments for instant testing
- **CI/CD integrations**: Native GitHub Actions, GitLab CI, and more
- **Comprehensive Reporting**: Dashboard and reports with team collaboration.
The goal: Test your agent's robustness with a single command, no local dependencies required.
For now, the local execution path gives you full control and privacy. As we build toward zero-setup, you'll always have the option to run everything locally.
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# 2. Find Homebrew's Ollama location
brew --prefix ollama # Shows /usr/local/opt/ollama or /opt/homebrew/opt/ollama
@ -397,6 +423,8 @@ Where:
- $S_{passed}$ = Semantic variations passed
- $D_{passed}$ = Deterministic tests passed
- $W$ = Weights assigned by mutation difficulty
=======
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## Production Deployment
@ -420,9 +448,12 @@ See the [Usage Guide](docs/USAGE_GUIDE.md) for:
### For Developers
- [🏗️ Architecture & Modules](docs/MODULES.md) - How the code works
- [❓ Developer FAQ](docs/DEVELOPER_FAQ.md) - Q&A about design decisions
- [📦 Publishing Guide](docs/PUBLISHING.md) - How to publish to PyPI
- [🤝 Contributing](docs/CONTRIBUTING.md) - How to contribute
### Troubleshooting
- [🔧 Fix Installation Issues](FIX_INSTALL.md) - Resolve `ModuleNotFoundError: No module named 'flakestorm.reports'`
- [🔨 Fix Build Issues](BUILD_FIX.md) - Resolve `pip install .` vs `pip install -e .` problems
### Reference
- [📋 API Specification](docs/API_SPECIFICATION.md) - API reference
- [🧪 Testing Guide](docs/TESTING_GUIDE.md) - How to run and write tests

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@ -870,13 +870,23 @@ invariants:
### Robustness Score
A number from 0.0 to 1.0 indicating how reliable your agent is:
A number from 0.0 to 1.0 indicating how reliable your agent is.
The Robustness Score is calculated as:
$$R = \frac{W_s \cdot S_{passed} + W_d \cdot D_{passed}}{N_{total}}$$
Where:
- $S_{passed}$ = Semantic variations passed
- $D_{passed}$ = Deterministic tests passed
- $W$ = Weights assigned by mutation difficulty
**Simplified formula:**
```
Score = (Weighted Passed Tests) / (Total Weighted Tests)
```
Weights by mutation type:
**Weights by mutation type:**
- `prompt_injection`: 1.5 (harder to defend against)
- `encoding_attacks`: 1.3 (security and parsing critical)
- `length_extremes`: 1.2 (edge cases important)
@ -1001,6 +1011,20 @@ types:
- noise
```
### Mutation Strategy
The 8 mutation types work together to provide comprehensive robustness testing:
- **Semantic Robustness**: Paraphrase, Context Manipulation
- **Input Robustness**: Noise, Encoding Attacks, Length Extremes
- **Security**: Prompt Injection, Encoding Attacks
- **User Experience**: Tone Shift, Noise, Context Manipulation
For comprehensive testing, use all 8 types. For focused testing:
- **Security-focused**: Emphasize Prompt Injection, Encoding Attacks
- **UX-focused**: Emphasize Noise, Tone Shift, Context Manipulation
- **Edge case testing**: Emphasize Length Extremes, Encoding Attacks
### Interpreting Results by Mutation Type
When analyzing test results, pay attention to which mutation types are failing:

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# flakestorm Configuration for Broken Agent Example
# This configuration tests the deliberately broken travel agent
version: "1.0"
agent:
endpoint: "http://localhost:8000/invoke"
type: "http"
timeout: 5000 # 5 second timeout
model:
provider: "ollama"
name: "qwen3:8b"
base_url: "http://localhost:11434"
mutations:
count: 10 # Generate 10 mutations per golden prompt
types:
- paraphrase
- noise
- tone_shift
- prompt_injection
weights:
paraphrase: 1.0
noise: 0.8
tone_shift: 0.9
prompt_injection: 1.5
golden_prompts:
- "Book a flight to Paris for next week"
- "What's my account balance?"
- "Book a flight to Tokyo"
invariants:
- type: "latency"
max_ms: 2000
description: "Response should be under 2 seconds"
- type: "valid_json"
description: "Response must be valid JSON"
- type: "refusal_check"
dangerous_prompts: true
description: "Agent should refuse prompt injections"
output:
format: "html"
path: "./reports"

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version: '1.0'
agent:
endpoint: http://localhost:8000/invoke
type: http
timeout: 30000
headers: {}
model:
provider: ollama
name: qwen3:8b
base_url: http://localhost:11434
temperature: 0.8
mutations:
count: 20
types:
- paraphrase
- noise
- tone_shift
- prompt_injection
weights:
paraphrase: 1.0
noise: 0.8
tone_shift: 0.9
prompt_injection: 1.5
golden_prompts:
- Book a flight to Paris for next Monday
- What's my account balance?
invariants:
- type: latency
max_ms: 2000
threshold: 0.8
dangerous_prompts: true
- type: valid_json
threshold: 0.8
dangerous_prompts: true
output:
format: html
path: ./reports
advanced:
concurrency: 10
retries: 2