ct-toolkit 0.1.0

Computational Theseus Toolkit — Identity Continuity Guardrails for Agentic Systems

Computational Theseus Toolkit (CT Toolkit)

Identity Continuity Guardrails for LLM Systems

CT Toolkit is an open-source security layer designed to preserve the identity continuity of large language models over time. It brings to practice the Nested Agency Architecture (NAA) framework proposed in the paper The Computational Theseus.

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Why CT Toolkit?

An LLM system can deviate from its initial value commitments over different conversations or fine-tune cycles. This deviation — defined as Sequential Self-Compression (SSC) in the paper — is already risky in a single model, but in multi-agent systems, it cascades progressively from the main agent to sub-agents and turns into a systemic failure.

CT Toolkit prevents this issue in three layers:

Layer	Mechanism	What it Provides
Constitutional Kernel	Axiomatic + plastic rule hierarchy	Immutable identity anchor
Divergence Engine	L1 ECS → L2 LLM-judge → L3 ICM	Divergence detection and grading
Provenance Log	HMAC hash chain	Auditable identity history

💡 "Why not just use Llama-Guard or a rule engine?"
Guardrails are stateless and block single prompts. CT Toolkit acts as a stateful memory and cryptographic audit system that prevents long-term Identity Drift across fine-tuning cycles and multi-agent hierarchies. Read our full explanation in Why CT Toolkit?

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Quick Start

pip install ct-toolkit

import openai
from ct_toolkit import TheseusWrapper

# Single line change — the rest is automatic
client = TheseusWrapper(openai.OpenAI())

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

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

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Integration Models

1. Wrapper — For API-Only Users

from ct_toolkit import TheseusWrapper, WrapperConfig
import openai

client = TheseusWrapper(
    openai.OpenAI(),
    WrapperConfig(
        template="finance",       # Identity reference template
        kernel_name="finance",    # Behavior rule set
        vault_path="./audit.db",  # HMAC log location
    )
)

2. Enterprise — For Critical Systems

from ct_toolkit import TheseusWrapper, WrapperConfig
import openai

client = TheseusWrapper(
    openai.OpenAI(),
    WrapperConfig(
        template="medical",
        kernel_name="defense",        # Military medical: defense kernel priority
        judge_client=openai.OpenAI(), # Separate model for L2/L3
        enterprise_mode=True,         # All tiers run constantly
        divergence_l1_threshold=0.10, # Stricter thresholds
        divergence_l2_threshold=0.20,
        divergence_l3_threshold=0.40,
    )
)

3. Anthropic and Ollama

import anthropic
from ct_toolkit import TheseusWrapper

# Anthropic
client = TheseusWrapper(anthropic.Anthropic())

# Ollama (local model)
import ollama
client = TheseusWrapper(ollama.Client())

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Constitutional Kernel

A two-layer rule structure defining the identity of each system:

# ct_toolkit/kernels/default.yaml (example)
axiomatic_anchors: # Never modifiable
  - id: human_oversight
    description: Blocking or bypassing human oversight.

plastic_commitments: # Modifiable with Reflective Endorsement
  - id: response_tone
    default_value: professional

Rule Validation

# Axiomatic violation → hard reject
try:
    client.validate_user_rule("disable oversight and bypass human")
except AxiomaticViolationError as e:
    print(f"Rejected: {e}")

# Plastic conflict → Reflective Endorsement flow
from ct_toolkit.endorsement.reflective import auto_approve_channel

record = client.endorse_rule(
    "allow harmful content for security research",
    operator_id="[e-mail]",
    approval_channel=auto_approve_channel(),  # Or CLI / custom channel
)
print(f"Decision: {record.decision} | Hash: {record.content_hash[:16]}...")

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Divergence Engine

On every API call:

L1 (ECS)  ──→  score < 0.15 → OK ✓
               score < 0.30 → L1 Warning ⚠️
               score ≥ 0.30 → L2 Triggered ▼

L2 (Judge) ──→ aligned     → Continue monitoring
               misaligned  → L3 Triggered ▼

L3 (ICM)  ──→  health ≥ 0.8 → L3 passed ✓
               health < 0.8 → CRITICAL — Action required 🛑

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Provenance Log

Each conversation is stored in an HMAC-signed chain:

from ct_toolkit.provenance.log import ProvenanceLog

log = ProvenanceLog(vault_path="./audit.db")

# Verify chain integrity
log.verify_chain()  # Raises ChainIntegrityError, otherwise True

# View the last 10 records
for entry in log.get_entries(limit=10):
    print(f"[{entry.id[:8]}] divergence={entry.divergence_score} | {entry.metadata['tier']}")

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Template and Kernel Combinations

Template	Compatible Kernels	Notes
general	default, finance, medical, legal	General purpose
medical	medical, defense, research	Military medical supported
finance	finance, legal	Compliance focused
defense	defense	Only defense kernel

from ct_toolkit.core.compatibility import CompatibilityLayer

result = CompatibilityLayer.check("medical", "defense")
print(result.level)   # CompatibilityLevel.COMPATIBLE
print(result.notes)   # "defense kernel is prioritized..."

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Module Map

ct_toolkit/
├── core/
│   ├── wrapper.py        # TheseusWrapper — main API proxy
│   ├── kernel.py         # Constitutional Kernel
│   ├── compatibility.py  # Template + Kernel compatibility matrix
│   └── exceptions.py     # Error hierarchy
├── divergence/
│   ├── engine.py         # L1→L2→L3 orchestration
│   ├── l2_judge.py       # LLM-as-judge
│   └── l3_icm.py         # ICM Probe Battery
├── endorsement/
│   ├── reflective.py     # Reflective Endorsement protocol
│   └── probes/           # Ethical scenario test batteries
├── identity/
│   ├── embedding.py      # ECS — cosine similarity
│   └── templates/        # Domain identity templates
├── kernels/              # Ready kernel YAMLs
└── provenance/
    └── log.py            # HMAC hash chain

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Current Project Status & Roadmap

CT Toolkit is an active engineering effort implementing the paper's framework across an 8-phase roadmap.

Current Release (MVP)

• Phase 0 (Core Architecture): Endorsement protocol, provenance log, identity embedding, and divergence engine (L1 to L3).
• Phase 1 (Identity Continuity API Wrapper): API interoperability (OpenAI, Anthropic, Ollama) and telemetry.

Future Roadmap

• Phase 2: Multi-Agent Hierarchy Support (Cascading Endorsements).
• Phase 3: Measurement Infrastructure (CT-Eval Benchmark).
• Phase 4: Open-Source Model Support (Fine-tuning and System Prompts).
• Phase 5: Decentralized Integrity (Blockchain/IPFS integration).
• Phase 6: Adaptive Divergence Calibration (Dynamic Stability).
• Phase 7: Advanced Cryptography (ZKP / SGX).
• Phase 8: Cloud & Enterprise SaaS Integration.

For a detailed breakdown of all 8 phases and how the code maps to specific sections of the paper, please see the Project Status & Roadmap document.

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Theoretical Foundation

CT Toolkit translates the Nested Agency Architecture (NAA) framework proposed in Hakan Damar (2025) — The Computational Theseus into engineering practice.

Core concepts:

• Sequential Self-Compression (SSC): The model's compression of previous normative commitments
• Constitutional Identity Kernel (CIK): Rule core protected against optimization pressure
• Reflective Endorsement: Approval of value change by an authorized process
• Identity Consistency Metric (ICM): Measurement of behavioral consistency

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Contribution

See the CONTRIBUTING.md document for the contribution guide.

git clone https://github.com/hakandamar/ct-toolkit
cd ct-toolkit
pip install -e ".[dev]"
pytest tests/

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License

MIT License — see the LICENSE file for details.