The Code Atelier Governance SDK (code-atelier-governance) is an MIT-licensed Python SDK that wraps LLM clients and tool calls in pre-execution enforcement gates (scope, budget, approvals, loop detection, presence) and writes an HMAC-chained audit trail to the Postgres your application already runs.
Its thesis is that tracing tools explain what an agent did after the damage, so the useful primitive is a gate that fires before the LLM call and denies it, backed by an audit chain a regulator can verify without a new data platform.
What it is #
pip install code-atelier-governance exposes a GovernanceSDK with eight modules: Audit, Scope, Cost, Gates, Loop Detection, Presence, Contracts, and Compliance.
Scope is a per-agent allowlist with hidden tools removed from the LLM context and default deny; Cost enforces token and USD caps per session and per agent-day with built-in pricing for more than twenty models and a combined budget query; Gates issues single-use HMAC-bound approval tokens for high-risk actions.
The audit trail is an append-only HMAC chain with optional Ed25519 per-row signatures, on-demand chain verification, and EU AI Act Article 12 evidence reports.
Integrations are one-line wrappers for OpenAI, Anthropic, and LangChain, plus a sync wrapper for Flask and Django, and everything depends only on a Postgres connection string, with an optional read-only console.
The vendor is Code Atelier, and the source lives at imleopereira/agentic-governance.
Status #
Active but essentially unadopted. The SDK is at version 0.7.3 with 11 PyPI releases, and the repository was created 2026-04-09 with a last push on 2026-07-23 as of 2026-09-27. The repository has 0 stars, 0 forks, and no description or topics, which is unusual for a project with a polished documentation site and a hosted platform bridge. The community footprint is absent: a Hacker News search for Code Atelier governance returns nothing, so every claim here rests on the vendor’s own pages and the repository. What earns the note despite the silence is the threat model, which states plainly what the SDK does not protect against instead of implying it is a complete security boundary. The vendor also publishes a scaffolder that wires a Microsoft Agent Governance Toolkit agent through these gates, which is a rare, concrete interoperability gesture in this category.
Strengths #
- Just Postgres: no Redis, Kafka, ClickHouse, sidecar, or background worker, so adoption cost is close to zero for a team already on Postgres.
- Fail-closed enforcement by default (cost, scope, and gates raise), while observation surfaces warn and continue, which is the right asymmetry.
- A written threat model that names the bypasses (direct client calls, subprocesses, tool calls inside LLM responses) rather than hiding them.
- EU AI Act Article 12 evidence export and a self-approval prevention design show the compliance audience is being taken seriously.
Cautions #
- 0 stars and no third-party usage evidence; the only assessment available is the vendor’s own.
- In-process gates do not defend against a second process or any code path that calls OpenAI or Anthropic directly, and the README says so; network-level enforcement needs a proxy.
- Scope enforcement gates the LLM call, not the tool calls returned inside the response, so a determined agent can still execute a disallowed tool unless you enforce at the tool layer too.
- Tampering is detected only when verification runs; a database administrator with key control can still replace the chain, as the threat model admits.
- v0.6.2 flipped several defaults to fail-closed and broke callers, so read the upgrade notes before following an older tutorial.
Pricing #
MIT licensed and free. A hosted platform exists through an opt-in bridge that dual-writes audit events while keeping local Postgres authoritative, plus a read-only console, but no prices are published.
Compared to #
- Veto: a framework-agnostic authorization kernel with a commercial cloud and portable receipts; choose Veto for TypeScript or polyglot stacks and this SDK when you are Python and Postgres and want budget and loop gates too.
- Microsoft Agent Governance Toolkit: broad, vendor-backed, and multi-language with identity and sandboxing; choose AGT for a governance program, and this SDK for the smallest viable gate.
- Langfuse: observability that records what happened; choose Langfuse to understand agent behavior, and this SDK to block a call before it happens.
Bottom line #
Recommended for Python teams already on Postgres that want a minimal pre-execution gate with budgets, approvals, and a tamper-evident audit trail. Not for polyglot stacks, teams needing process or network-level enforcement, or anyone who requires an adopted project with independent security review.
Changes #
- 2026-09-27 - Created.
See also #
- Control Planes Feature Matrix - the category compared on shared rows
- Veto - the authorization-kernel alternative with a commercial cloud
- Microsoft Agent Governance Toolkit - the broad multi-language alternative
- An Agent Is Only as Safe as Its Worst Tool Call - the corpus argument for gating the call
- Evaluation Review Feature Matrix - the observability layer this deliberately is not
References #
https://www.codeatelier.tech/governance - overview: modules, comparison table, Article 12 framing
https://www.codeatelier.tech/governance/quickstart - quickstart: install, schema, gates, console, sync wrapper
https://github.com/imleopereira/agentic-governance - README: modules, threat model, configuration, standards
https://api.github.com/repos/imleopereira/agentic-governance - stars, forks, creation and push dates as of 2026-09-27
https://pypi.org/pypi/code-atelier-governance/json - version 0.7.3, MIT license, 11 releases
https://raw.githubusercontent.com/imleopereira/agentic-governance/production/CHANGELOG.md - release notes: fail-closed defaults and the AGT recipe scaffolder