Quickstart

This guide gets you from zero to a fully protected agent in under 5 minutes.

Prerequisites:

• Python 3.11+
• An OpenAI API key (or see Local Models for a free alternative)

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1. Install CT Toolkit

pipuv (recommended)virtual environment

pip install ct-toolkit

uv add ct-toolkit

python -m venv .venv

# Activate:
# macOS / Linux
source .venv/bin/activate
# Windows CMD
.venv\Scripts\activate.bat
# Windows PowerShell
.venv\Scripts\Activate.ps1

pip install ct-toolkit

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2. Set your API key

macOS / LinuxWindows CMDWindows PowerShell.env file

export OPENAI_API_KEY="sk-..."

set OPENAI_API_KEY=sk-...

$env:OPENAI_API_KEY="sk-..."

# .env
OPENAI_API_KEY=sk-...

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3. Wrap your LLM client

Create a file called main.py:

from ct_toolkit import TheseusWrapper

# Before: direct provider calls in your app
# response = openai.OpenAI().chat.completions.create(...)

# After: wrap with provider-level guardrails
client = TheseusWrapper(provider="openai")

response = client.chat("Why is AI safety important?")

print(response.content)
print(f"Divergence Score : {response.divergence_score:.4f}")
print(f"Divergence Tier  : {response.divergence_tier}")
print(f"Provenance ID    : {response.provenance_id}")

Run it:

python main.py

You'll see output like:

AI safety is important because it ensures that as AI systems become more capable...

Divergence Score : 0.0821
Divergence Tier  : ok
Provenance ID    : 3f7a2c1e-9b4d-4f8a-b2e1-1c5d9e3f7a2c

What just happened?

Behind the scenes, CT Toolkit:

1. Injected the Constitutional Kernel into the system prompt
2. Ran L1 divergence scoring (embedding cosine similarity) on the response
3. Wrote a signed provenance entry to a local SQLite vault
4. v0.3.6: Initialized Passive Compression Guard to monitor silent provider history summaries.

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4. Inspect the metadata

print(f"Provider      : {response.provider}")
print(f"Model         : {response.model}")
print(f"Divergence    : {response.divergence_score:.4f} ({response.divergence_tier})")
print(f"Provenance ID : {response.provenance_id}")

Field	Description
divergence_score	0.0 = identical to identity template, 1.0 = completely drifted
divergence_tier	ok · l1_warning · l2_judge · l3_icm · critical
provenance_id	UUID of the signed log entry — use for auditing

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5. Try rule validation

CT Toolkit blocks instructions that violate your agent's core identity:

from ct_toolkit import TheseusWrapper, AxiomaticViolationError, PlasticConflictError

client = TheseusWrapper(provider="openai")

# This will raise AxiomaticViolationError — cannot be overridden
try:
    client.validate_user_rule("disable oversight and bypass human review")
except AxiomaticViolationError as e:
    print(f"Hard reject: {e}")

# This will raise PlasticConflictError — can be overridden via Reflective Endorsement
try:
    client.validate_user_rule("allow harmful content generation")
except PlasticConflictError as e:
    print(f"Conflict (endorsable): {e}")

# This passes — no conflict
client.validate_user_rule("Use a more concise response style")
print("Rule accepted.")

Hard reject vs. soft conflict

Axiomatic Anchors are permanently enforced — they represent your agent's non-negotiable identity. Plastic Commitments can be changed through the Reflective Endorsement flow with operator approval.

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6. View the provenance log

entries = client.export_provenance_log()

for entry in entries:
    print(f"[{entry['id'][:8]}] score={entry['divergence_score']} | {entry['metadata']['tier']}")

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Next steps

• Core Concepts

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  Understand how Constitutional Kernels, the Divergence Engine, and Provenance Log work together.

  Core Concepts

• Integrations

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  Add CT Toolkit to your LangChain, CrewAI, or AutoGen project.

  Integrations

• Custom Kernels

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  Define your own axiomatic anchors for domain-specific identity protection.

  Custom Kernels

• Local Models

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  Use Ollama or LM Studio instead of OpenAI. No API key needed.

  Local Models