Vitnify your agents

Logs tell you what your agent did. Vitnify proves it — a cryptographic, independently-reconstructable record of what an agent computed and did.

Contain what an agent may do, deterministically reconstruct the model behind every decision, and seal the whole run into one bit-for-bit receipt anyone can verify offline — long after it happened.

vitnify (v.) — to turn an agent run into a receipt anyone can reproduce and verify, offline.

vitnify isn't detection. It gives you the primitives to prove exactly what an agent did: a vitnify-receipt v2 binds the model's computation, the granted capabilities, every tool call and result, the entropy, and the order into a single ed25519-signed, self-verifying object.

Install

pip install vitnify

Quickstart

from vitnify.events import EventLog, Kind
from vitnify.engine import Engine, prompt_hash
from vitnify.certificate import issue_certificate, verify_certificate, gen_ed25519

eng = Engine("model.gguf", model_id="my-model")        # deterministic backend
log = EventLog()

step = eng.run(prompt_tokens=[1, 2, 3], n_new=20)       # a model step
log.append_llm_call(prompt_hash([1, 2, 3]), step["tokens"], seed=0,
                    model_digest=step["model_digest"])   # bind the model computation
log.append(Kind.TOOL_CALL, {"tool": "read_docs",  "decision": "allow"})
log.append(Kind.TOOL_CALL, {"tool": "send_email", "decision": "deny"})  # ungranted → blocked

priv, pub = gen_ed25519()
cert, _ = issue_certificate("program_hash", ["read_docs"], log, priv=priv)

checks = verify_certificate(cert, log)   # level 1: offline integrity — no model, no secret
assert checks["ok"]                       # signed, unaltered, and no ungranted tool ran
assert checks["containment_enforced"]     # every tool call was GATED, not merely observed
# A receipt can be ok=True yet containment_enforced=False — a valid transcript from a
# watch-only integration proves what ran, not that anything was contained. A containment
# claim requires BOTH. (level 2: re-run each step through the engine; every model_digest
# reproduces bit-for-bit.)

See vitnify-receipt-v2.md for the receipt format, and examples/demo_receipt_e2e.py for the full loop.

What you get

• Capability containment — ungranted tools are structurally unreachable.
• Deterministic replay — re-run any past run and get the identical result.
• Bit-for-bit receipts — the model's exact computation, bound and signed.
• Offline verification — anyone verifies with no model, network, or secret.
• Drop-in — wraps existing LangGraph and MCP agents (pip install vitnify[langgraph] / [mcp]).

The deterministic engine is vitni-tensor; the vitni-receipt binary is the model backend (point VITNI_RECEIPT_BIN at it).

License

Apache-2.0. "vitnify" and "vitnify-verified" are trademarks — see TRADEMARKS.md. A fork may use the code, but not the name or issue vitnify-verified receipts.

Part of Vitnify

This SDK is one of three open repos:

• vitni-tensor — the deterministic, no_std engine that produces the bit-identical model-computation digest this SDK binds.
• vitnify-receipt-spec — the canonical vitnify-receipt v2 format the SDK implements.
• vitnify.com — the project.