phylax 1.1.4

Deterministic regression enforcement for LLM systems.

Phylax

Deterministic regression enforcement for LLM systems.

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The Problem

LLM outputs change unexpectedly. Same prompt, different model version → different behavior. Without Phylax, you discover this in production.

Installation

pip install phylax

For all LLM providers (OpenAI, Gemini, Groq, Mistral, HuggingFace, Ollama):

pip install phylax[all]

Individual providers:

pip install phylax[openai]       # OpenAI
pip install phylax[google]       # Gemini (google-genai SDK)
pip install phylax[groq]         # Groq LPU
pip install phylax[mistral]      # Mistral AI
pip install phylax[huggingface]  # HuggingFace Inference API
pip install phylax[ollama]       # Ollama (local models)

Quick Start

from phylax import trace, expect, execution, GeminiAdapter

@trace(provider="gemini")
@expect(must_include=["hello"], max_latency_ms=5000)
def greet(name: str):
    """Traced Gemini call with expectations."""
    adapter = GeminiAdapter()
    response, _ = adapter.generate(
        prompt=f"Say hello to {name}",
        model="gemini-2.5-flash",
    )
    return response

# Single call
result = greet("World")
print(result.text)

# Track multi-step agent flows
with execution() as exec_id:
    step1 = greet("Alice")
    step2 = greet("Bob")

# Start the UI server
phylax server
# Open http://127.0.0.1:8000/ui

# Mark a known-good response as baseline
phylax bless <trace_id>

# In CI: fail if output regresses
phylax check  # exits 1 on failure

That's it. Your CI now blocks LLM regressions.

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Supported Providers

Provider	Adapter	Env Variable
OpenAI	OpenAIAdapter	OPENAI_API_KEY
Gemini	GeminiAdapter	GOOGLE_API_KEY
Groq	GroqAdapter	GROQ_API_KEY
Mistral	MistralAdapter	MISTRAL_API_KEY
HuggingFace	HuggingFaceAdapter	HF_TOKEN
Ollama	OllamaAdapter	OLLAMA_HOST

from phylax import OpenAIAdapter, GroqAdapter, MistralAdapter

# All adapters share the same interface
adapter = GroqAdapter()
response, trace = adapter.generate(prompt="Hello!", model="llama3-70b-8192")

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What Phylax is NOT

• ❌ Not monitoring — no metrics, no dashboards
• ❌ Not observability — no traces-to-cloud, no analytics
• ❌ Not AI judgment — rules are deterministic, not LLM-based
• ❌ Not cloud-dependent — runs entirely local
• ❌ Not prompt engineering — tests outputs, not prompts

Phylax is a test framework. It tells you when LLM behavior changes.

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CI Integration

# .github/workflows/phylax.yml
- run: phylax check
  env:
    GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}

Exit codes:

• 0 — All golden traces pass
• 1 — Regression detected

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Commands

Command	What it does
phylax init	Initialize config
phylax server	Start API server + UI
phylax list	List traces
phylax list --failed	Show only failed traces
phylax show <id>	Show trace details
phylax replay <id>	Re-run a trace
phylax bless <id>	Mark as golden baseline
phylax check	CI regression check

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Features

Feature	Description
Trace Capture	Record every LLM call automatically
Expectations	Validate with @expect rules
Execution Context	Group traces by execution() context
Golden Traces	Baseline comparisons with hash verification
CI Integration	phylax check exits 1 on regression
Web UI	View traces at http://127.0.0.1:8000/ui
Multi-Provider	OpenAI, Gemini, Groq, Mistral, HuggingFace, Ollama

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Demos

See the demos/ directory for runnable examples:

python demos/01_basic_trace.py      # Basic tracing
python demos/02_expectations.py     # All @expect rules
python demos/03_execution_context.py # Trace grouping
python demos/04_graph_nodes.py      # Graph API
python demos/05_golden_workflow.py  # CI workflow
python demos/06_raw_evidence.py     # Evidence API
python demos/07_error_contracts.py  # Error codes

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Documentation

• Quickstart
• Providers
• Error Codes
• Correct Usage
• API Contract

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License

MIT License