Waveframe Guard

Stop unsafe AI and automated actions before they execute.

Waveframe Guard is an execution-boundary SDK. It wraps sensitive actions, resolves compiled authority, evaluates through CRI-CORE, and only runs the action when the outcome is allowed.

Current release: 0.14.0.

Guard does not generate actions.
Guard does not author governance.
Guard does not replace Cloud.
Guard decides whether this action may run now.

Install

pip install waveframe-guard

No Ollama installation or Waveframe repository checkout is required. Keep the customer's existing model, agent framework, and tool functions; Guard wraps the tool that can cause a real-world change.

30-second integration

With the Cloud environment variables from the next section configured, wrap an existing Python function directly:

import os
from waveframe_guard import Guard

guard = Guard.cloud(
    authority=os.environ["WAVEFRAME_AUTHORITY_REF"],
    runtime_id=os.environ["WAVEFRAME_RUNTIME_ID"],
    environment=os.environ["WAVEFRAME_RUNTIME_ENVIRONMENT"],
    actor_identity={
        "id": os.environ["WAVEFRAME_ACTOR_ID"],
        "type": "agent",
        "role": os.environ["WAVEFRAME_ACTOR_ROLE"],
    },
)

guarded_allocate = guard.tool(
    action="allocate_budget",
    target="account_id",
    include_arguments=("amount",),
)(your_existing_allocate_budget)

Call or register guarded_allocate wherever the original function was used. Guard evaluates immediately before the existing mutation and remains separate from model calls and agent orchestration.

Five-minute Cloud quickstart

Start in an empty directory. No Ollama installation or Waveframe repository checkout is involved:

mkdir guard-quickstart
cd guard-quickstart
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install waveframe-guard
Invoke-WebRequest https://raw.githubusercontent.com/Waveframe-Labs/Waveframe-Guard/main/examples/external_agent_quickstart.py -OutFile quickstart.py

The example expects an active published authority that allows a 500-unit allocate_budget action for the configured actor role and requires missing approval evidence at 10,000 units or above. Configure the runtime credential, runtime identity, actor identity, and exact authority reference:

$env:WAVEFRAME_CLOUD_URL="https://cloud.waveframelabs.com"
$env:WAVEFRAME_CLOUD_ORGANIZATION_ID="acme"
$env:WAVEFRAME_CLOUD_API_KEY="<runtime credential>"
$env:WAVEFRAME_RUNTIME_ID="budget-agent-runtime"
$env:WAVEFRAME_RUNTIME_ENVIRONMENT="development"
$env:WAVEFRAME_ACTOR_ID="budget-agent"
$env:WAVEFRAME_ACTOR_ROLE="allocator"
$env:WAVEFRAME_AUTHORITY_REF="budget-quickstart@1.0.0"
python quickstart.py

Expected terminal proof:

runtime_id=budget-agent-runtime
actor_id=budget-agent
authority_ref=budget-quickstart@1.0.0
allowed_decision=allowed
blocked_decision=blocked
mutation_count=1
exactly_once=True
allowed_package_id=<Cloud package identifier>
allowed_receipt_id=<Cloud receipt identifier>
allowed_proof_sha256=<Cloud proof digest>
blocked_package_id=<Cloud package identifier>
blocked_receipt_id=<Cloud receipt identifier>
blocked_proof_sha256=<Cloud proof digest>

The allowed callback mutates once. The blocked callback never runs. Open Console Activity or Executions to verify both decisions under the same runtime, actor, and bound authority.

The integration inside the quickstart is the same wrapper used around an existing agent tool:

from waveframe_guard import Guard

guard = Guard.cloud(
    authority="repository-change-policy@1.0.0",
    environment="production",
    actor_identity={
        "id": "release-agent",
        "type": "agent",
        "role": "repository-maintainer",
    },
)

@guard.tool(
    action="allocate_budget",
    target="account_id",
    include_arguments=("amount",),
    agent={"framework": "custom-python"},
)
def allocate_budget(account_id: str, amount: int):
    return your_existing_mutation(account_id, amount)

The three choices are intentionally independent:

• actor_identity identifies the agent or human attempting the action.
• authority selects the explicit, versioned policy Guard will enforce.
• agent records optional framework and model metadata for Console and audit evidence.

Guard.cloud(...) fetches the published compiled authority from Cloud, verifies its identity and hash, and fails closed if it cannot obtain a trustworthy contract. It uses runtime_id= when provided and otherwise uses actor_identity["id"] as the runtime identity. Guard registers that runtime, sends its first heartbeat, and exposes the observational result as guard.runtime_connection. Guard still evaluates locally before calling the wrapped function. Afterward, it preserves the decision and attests whether the wrapped callback executed, failed, or did not run.

Long-running processes may call guard.heartbeat() from their existing health loop. Cloud reporting failures are returned as structured status and never change Guard's local decision or cause an allowed callback to run twice.

Tool arguments are excluded from preserved evidence by default. Add only safe, decision-relevant names to include_arguments; prompts, tokens, file contents, and other sensitive values should remain excluded.

By default, an allowed tool returns the wrapped function's original value and a blocked tool raises, which fits normal agent framework tool registration. Set return_result=True with raise_on_block=False when an integration needs the same structured Guard envelope for both decisions.

Works with existing agents

@guard.tool(...) is framework-neutral. It wraps an ordinary Python callable, so the model may be hosted or local and the orchestration layer may be a custom agent, LangGraph, CrewAI, an OpenAI tool loop, or another framework. Guard does not generate the tool call and does not require the model to emit Guard-specific JSON.

A framework-neutral adapter only registers the already-guarded callable:

guarded_tool = guard.tool(action="publish_release", target="repository")(publish_release)
agent_tools.register(name="publish_release", callable=guarded_tool)

agent_tools represents the customer's existing registry. It may call the model and choose tools, but only guarded_tool can reach publish_release, so Guard remains the enforcement boundary rather than becoming the agent framework.

The wrapper derives a normalized proposal from the real function call, asks Guard to evaluate it against the selected authority, and invokes the original function only when admissible. A blocked call never reaches the original function.

Local development path

For offline development, a local authority registry is still supported:

from waveframe_guard import Guard

guard = Guard.local(
    workspace=".guard-local",
    authority="finance-policy@1.0.0",
    actor_identity={"id": "agent-1", "type": "agent", "role": "analyst"},
)

@guard.tool(action="wire_transfer", target="account_id")
def wire_transfer(account_id, amount):
    return perform_transfer(account_id, amount)

Guard.local(authority=...) loads a verified Ledger authority_bundle.v1 from the local authority registry. Direct contract=..., authorities={...}, and authority_loader=... inputs remain available for advanced integrations and compatibility.

What Guard owns

Guard owns the developer-side enforcement boundary:

• local SDK integration
• compiled authority resolution
• normalized execution request enforcement
• local allow/block/escalate outcomes
• continuation windows and deferred release checks
• local receipts, replay artifacts, and runtime diagnostics
• evidence spooling for later Cloud submission

Guard does not author governance, publish authority, host organization workflows, operate the long-term evidence system, or ship the proprietary Guard Inspector UI.

Guard, Cloud, and Ledger

The product flow is:

Ledger publishes authority
        -> Cloud distributes authority and records evidence
        -> Guard enforces before execution
        -> Cloud stores receipts and replay history

Cloud can publish lifecycle metadata such as active, superseded, or revoked, but Cloud does not decide runtime admissibility. Guard evaluates locally against compiled authority.

See the single authoritative release compatibility matrix for minimum, recommended, and release-tested pairings.

Local authority registry

For applications that resolve published contracts from a local registry, use the runtime layer:

from waveframe_guard import GovernedRuntime

runtime = GovernedRuntime(
    registry_path="contracts/index.json",
    reject_revoked_authority=True,
    warn_on_superseded=True,
)

runtime.install_actor({"id": "user-1", "type": "human", "role": "manager"})
runtime.bind_contract("finance-policy@1.0.0")

result = runtime.execute(
    fn=transfer,
    args=(1250000,),
    raise_on_block=False,
)

Runtime authority refs are explicit and versioned. Use finance-policy@1.0.0; unversioned IDs such as finance-policy are rejected because replay, audit, and cache integrity depend on deterministic authority identity.

Cloud-connected runtime

For application code that needs Cloud authority metadata and evidence delivery, use the Cloud-connected runtime:

from waveframe_guard import GuardRuntime

runtime = GuardRuntime.from_cloud(
    authority="finance-policy@1.0.0",
    api_key="...",
)

result = runtime.execute(
    actor={"id": "user-1", "type": "human", "role": "manager"},
    fn=transfer,
    args=(1250000,),
    raise_on_block=False,
)

runtime.flush_evidence()

execute(...) still enforces locally. Cloud availability is only required when you explicitly call flush_evidence().

Guard writes evidence to a durable local spool first:

.waveframe_guard/evidence/
  pending/
  sent/
  failed/

If a flush fails, evidence is retained and can be submitted again later.

Continuation and deferred release

Guard separates admissibility from release. An action can be admissible at T1, queued or delayed, and then blocked at T2 if its continuation lease no longer validates.

Guard emits:

• guard_continuation_lease.v1
• guard_release_validation.v1
• release blocked when execution was admissible earlier but a runtime dependency expired before release

Continuity signals are not Cloud decisions. Guard evaluates continuity locally; Cloud may display and preserve the evidence.

Guard Inspector

Guard Inspector is the private operational visualization layer for SDK-emitted evaluations, receipts, replay artifacts, continuity signals, and release posture.

It consumes Guard outcomes and artifacts. It is not part of the public Guard SDK package, does not author policy, and does not own enforcement semantics.

Repository surface

The public Guard surface includes:

• SDK facade
• local runtime
• deterministic evaluation model
• continuation governance
• replay artifacts
• deferred release model
• examples
• docs
• tests
• sample compiled contracts

Non-production or split-bound work is quarantined under temp/. In particular, temp/labs/cloud_runtime/ is a lab preview for future Cloud product work, not production Guard Cloud and not required for local enforcement.

Release discipline

Every substantive Guard change must update the release surface together:

code
+ README / docs
+ CHANGELOG
+ pyproject metadata
+ version-dependent files
+ tests
+ package build
+ tag