tibet-core 0.3.0

Token-based Intent, Behavior, Evidence & Trust - Cryptographic provenance for trustworthy systems

tibet-core

Token-based Intent, Behavior, Evidence & Trust

Cryptographic provenance for trustworthy systems. Zero dependencies. Audit-ready.

Why TIBET?

Traditional security monitors traffic. TIBET audits actions.

Every function call, every decision, every transformation gets a cryptographic token with:

• ERIN (What's IN): The content/data of the action
• ERAAN (What's attached): References, dependencies
• EROMHEEN (What's around): Context, environment, state
• ERACHTER (What's behind): Intent, reason, purpose

Compliance Ready

TIBET provides the audit foundation for:

Standard	TIBET Support
ISO 5338	AI decision traceability
NIS2	Continuous logging, incident snapshots
BIO2	Government security baseline
OWASP	Security event provenance

Installation

pip install tibet-core

Quick Start

from tibet_core import Provider, FileStore

# Create provider with persistent storage
tibet = Provider(
    actor="jis:humotica:my_app",
    store=FileStore("./audit.jsonl")
)

# Record any action
token = tibet.create(
    action="user_login",
    erin={"user_id": "alice", "method": "oauth"},
    eraan=["jis:humotica:auth_service"],
    eromheen={"ip": "192.168.1.1", "user_agent": "Mozilla/5.0"},
    erachter="User authentication for dashboard access"
)

# Token has cryptographic integrity
assert token.verify()
print(token.content_hash)  # SHA-256

# Export audit trail
audit = tibet.export(format="jsonl")

Integration Examples

With rapid-rag (RAG/Search)

from rapid_rag import RapidRAG
from tibet_core import Provider

tibet = Provider(actor="jis:company:rag_system")
rag = RapidRAG("documents", tibet=tibet)

# All operations now have provenance
rag.add_file("contract.pdf")
results = rag.search("liability clause")
answer = rag.query("What are our obligations?")

# Full audit trail
for token in tibet.find(action="search"):
    print(f"{token.timestamp}: {token.erin['query']}")

With oomllama (LLM Routing)

from oomllama import Engine
from tibet_core import Provider

tibet = Provider(actor="jis:company:llm_router")

# Every LLM call is audited
response = engine.generate(
    prompt="Summarize this document",
    tibet=tibet
)

# Know which model answered, why, with what context

With comms-core-rs (Telephony)

// Rust: 0.02 second call setup WITH tibet exchange
let token = tibet.create(
    action: "call_initiated",
    erin: CallData { from, to, codec },
    erachter: "Outbound sales call"
);

Chain Tracing

Follow provenance chains:

from tibet_core import Chain

chain = Chain(tibet.store)

# Trace backwards from any token
history = chain.trace(token.token_id)
for t in history:
    print(f"{t.action}: {t.erachter}")

# Verify entire chain integrity
if chain.verify(token.token_id):
    print("Audit trail intact")

# Get chain summary
summary = chain.summary(token.token_id)
print(f"Chain length: {summary['length']}")
print(f"Actors involved: {summary['actors']}")

Storage Backends

MemoryStore (default)

Fast, ephemeral. Good for testing.

FileStore

Append-only JSONL. Audit-friendly. Tamper-evident.

from tibet_core import FileStore

store = FileStore("./audit.jsonl")

# Verify file integrity
result = store.verify_file()
if not result["integrity"]:
    print(f"Corrupted tokens: {result['corrupted_ids']}")

Performance

TIBET adds minimal overhead:

• Token creation: ~0.1ms
• Hash computation: ~0.05ms
• File append: ~0.2ms

In comms-core-rs, full call setup with TIBET exchange: 0.02 seconds

More code ≠ slower. Trust ≠ overhead.

Philosophy

"Audit de basis voor elke actie, niet voor communicatie verkeer"

"Audit as foundation for every action, not just traffic"

TIBET doesn't watch the wire. It lives inside the action.

License

MIT - Humotica

Links

• Humotica
• JIS Identity Standard
• rapid-rag
• oomllama