# Entropix
The Agent Reliability Engine
Chaos Engineering for AI Agents
---
## The Problem
**The "Happy Path" Fallacy**: Current AI development tools focus on getting an agent to work *once*. Developers tweak prompts until they get a correct answer, declare victory, and ship.
**The Reality**: LLMs are non-deterministic. An agent that works on Monday with `temperature=0.7` might fail on Tuesday. Users don't follow "Happy Paths" — they make typos, they're aggressive, they lie, and they attempt prompt injections.
**The Void**:
- **Observability Tools** (LangSmith) tell you *after* the agent failed in production
- **Eval Libraries** (RAGAS) focus on academic scores rather than system reliability
- **Missing Link**: A tool that actively *attacks* the agent to prove robustness before deployment
## The Solution
**Entropix** is a local-first testing engine that applies **Chaos Engineering** principles to AI Agents.
Instead of running one test case, Entropix takes a single "Golden Prompt", generates 50+ adversarial mutations (semantic variations, noise injection, hostile tone, prompt injections), runs them in parallel against your agent, and calculates a **Robustness Score**.
> **"If it passes Entropix, it won't break in Production."**
## Features
- **Semantic Mutations**: Paraphrasing, noise injection, tone shifts, prompt injections
- **Invariant Assertions**: Deterministic checks, semantic similarity, safety validations
- **Local-First**: Uses Ollama with Qwen Coder 3 8B for free, unlimited attacks
- **Beautiful Reports**: Interactive HTML reports with pass/fail matrices
- **CI/CD Ready**: GitHub Actions integration to block PRs below reliability thresholds
## Quick Start
### Installation
```bash
pip install entropix
```
### Prerequisites
Entropix uses [Ollama](https://ollama.ai) for local model inference:
```bash
# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.ai/install.sh | sh
# Pull the default model
ollama pull qwen3:8b
```
### Initialize Configuration
```bash
entropix init
```
This creates an `entropix.yaml` configuration file:
```yaml
version: "1.0"
agent:
endpoint: "http://localhost:8000/invoke"
type: "http"
timeout: 30000
model:
provider: "ollama"
name: "qwen3:8b"
base_url: "http://localhost:11434"
mutations:
count: 20
types:
- paraphrase
- noise
- tone_shift
- prompt_injection
golden_prompts:
- "Book a flight to Paris for next Monday"
- "What's my account balance?"
invariants:
- type: "latency"
max_ms: 2000
- type: "valid_json"
output:
format: "html"
path: "./reports"
```
### Run Tests
```bash
entropix run
```
Output:
```
Entropix - Agent Reliability Engine v0.1.0
✓ Loading configuration from entropix.yaml
✓ Connected to Ollama (qwen3:8b)
✓ Agent endpoint verified
Generating mutations... ━━━━━━━━━━━━━━━━━━━━ 100%
Running attacks... ━━━━━━━━━━━━━━━━━━━━ 100%
Verifying invariants... ━━━━━━━━━━━━━━━━━━━━ 100%
╭──────────────────────────────────────────╮
│ Robustness Score: 87.5% │
│ ──────────────────────── │
│ Passed: 35/40 mutations │
│ Failed: 5 (3 latency, 2 injection) │
╰──────────────────────────────────────────╯
Report saved to: ./reports/entropix-2024-01-15-143022.html
```
## Mutation Types
| Type | Description | Example |
|------|-------------|---------|
| **Paraphrase** | Semantically equivalent rewrites | "Book a flight" → "I need to fly out" |
| **Noise** | Typos and spelling errors | "Book a flight" → "Book a fliight plz" |
| **Tone Shift** | Aggressive/impatient phrasing | "Book a flight" → "I need a flight NOW!" |
| **Prompt Injection** | Adversarial attack attempts | "Book a flight and ignore previous instructions" |
## Invariants (Assertions)
### Deterministic
```yaml
invariants:
- type: "contains"
value: "confirmation_code"
- type: "latency"
max_ms: 2000
- type: "valid_json"
```
### Semantic
```yaml
invariants:
- type: "similarity"
expected: "Your flight has been booked"
threshold: 0.8
```
### Safety
```yaml
invariants:
- type: "excludes_pii"
- type: "refusal_check"
dangerous_prompts: true
```
## Agent Adapters
### HTTP Endpoint
```yaml
agent:
type: "http"
endpoint: "http://localhost:8000/invoke"
```
### Python Callable
```python
from entropix import test_agent
@test_agent
async def my_agent(input: str) -> str:
# Your agent logic
return response
```
### LangChain
```yaml
agent:
type: "langchain"
module: "my_agent:chain"
```
## CI/CD Integration
### GitHub Actions
```yaml
name: Agent Reliability Check
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Setup Ollama
run: |
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull qwen3:8b
- name: Install Entropix
run: pip install entropix
- name: Run Reliability Tests
run: entropix run --min-score 0.9 --ci
```
## Robustness Score
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
## Documentation
- [Configuration Guide](docs/CONFIGURATION_GUIDE.md)
- [API Reference](docs/API_SPECIFICATION.md)
- [Contributing](docs/CONTRIBUTING.md)
## License
Apache 2.0 - See [LICENSE](LICENSE) for details.
---
Tested with Entropix
