provena 0.14.0

Context governance for agentic AI — tamper-evident audit trails for LLM context inputs

Provena

Context governance for agentic AI systems. (MVP)

Your AI agent just made a decision based on data from 6 different sources. Can you tell me which ones? Can you prove the data wasn't tampered with? Can you verify it was still current?

Provena adds tamper-evident audit trails to any AI agent's context pipeline — in 3 lines of Python.

from provena import ContextTrail

trail = ContextTrail()

@trail.track(source="retriever")
def search(query):
    return retriever.search(query)

Every call to search() is now logged with a SHA-256 content hash, provenance validation, and a hash-chained audit trail that detects tampering.

Why Provena?

AGT governs what agents DO. Guardrails AI governs what agents SAY. Provena governs what agents KNOW.

No existing tool governs the context input layer. Provena fills this gap with:

• Tamper-evident audit trails — SHA-256 hash-chained (Merkle-style) logging with optional HMAC signing
• Provenance validation — Verify that context carries proper source metadata (VALID / MISSING / INCOMPLETE)
• Freshness checking — Detect stale context via metadata timestamps and regex temporal detection (FRESH / STALE / UNKNOWN)
• Any context source — RAG retrievers, tool outputs, agent messages, memory recalls, MCP resources
• Sub-1ms overhead — Pure Python, no ML models, no ONNX, no model downloads
• Zero dependencies — Core library uses only the Python standard library

Install

pip install provena              # core (zero dependencies)
pip install provena[cli]         # + CLI tools (click, rich)
pip install provena[otel]        # + OpenTelemetry export
pip install provena[langchain]   # + LangChain adapter
pip install provena[llamaindex]  # + LlamaIndex adapter
pip install provena[all]         # everything

Quick Start

from provena import ContextTrail, ProvenanceMetadata
from datetime import datetime, timezone

trail = ContextTrail(storage_path="audit.db")

# Track any function that produces context
@trail.track(source="retriever")
def search(query):
    return retriever.search(query)

@trail.track(source="tool:pricing_api")
def get_price(product_id):
    return api.get(f"/price/{product_id}")

# Manual logging with provenance metadata
trail.log(
    content="The enterprise plan costs $499/month.",
    source="tool:pricing_api",
    provenance=ProvenanceMetadata(
        source_url="https://api.example.com/pricing",
        created_at=datetime.now(timezone.utc),
    ),
)

# Verify the audit trail hasn't been tampered with
verdict = trail.verify_chain()
print(f"Chain intact: {verdict.intact}")
print(f"Total records: {verdict.total_records}")

CLI

Install with pip install provena[cli], then:

# Verify hash chain integrity
provena --db audit.db verify
# PASS — Chain intact (42 records verified)

# Query the audit log
provena --db audit.db audit --source retriever --format json

# Generate a governance report
provena --db audit.db report --format text

# Quick summary
provena --db audit.db summary

For HMAC-signed trails, pass --signing-key or set PROVENA_SIGNING_KEY.

Integrations

LangChain

from provena.integrations.langchain import ProvenaCallback

chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    callbacks=[ProvenaCallback(trail=trail)],
)

LlamaIndex

from provena.integrations.llamaindex import ProvenaPostprocessor

query_engine = index.as_query_engine(
    node_postprocessors=[ProvenaPostprocessor(trail=trail)]
)

OpenTelemetry

trail = ContextTrail(
    storage_path="audit.db",
    otel_enabled=True,
    otel_service_name="my-agent",
)
# Every log() call now emits an OTel span with governance attributes

Architecture

Your Application
│
│  Retriever ──┐
│  Tool Call ──┤
│  Agent Msg ──┼──► ContextTrail ──► LLM Context Window
│  Memory    ──┤        │
│  MCP       ──┘        │
│                  ┌─────┴──────────────┐
│                  │ ProvenanceValidator │
│                  │ FreshnessChecker    │
│                  │ HashChain (SHA-256) │
│                  │ SQLite Backend      │
│                  │ OTel Exporter       │
│                  └────────────────────┘

Compliance

Provena maps directly to EU AI Act requirements (enforcement: August 2, 2026):

Article	Requirement	Provena Feature
Art. 10	Data lineage	Provenance validation for every context input
Art. 12	Tamper-evident logging	SHA-256 hash-chained audit trail with HMAC signing
Art. 13	Transparency	trail.summary() and source tracking
Art. 14	Human oversight	trail.annotate() for reviewer decisions

Also addresses OWASP ASI06 (Memory & Context Poisoning).

Contributing

We welcome contributions! See CONTRIBUTING.md for development setup, architecture guide, and PR process.

Please read our Code of Conduct before participating.

License

Apache 2.0