Laguarde

Laguarde is a self-hostable policy control plane for AI coding agents.

It gives a team one persistent place to define engineering practices, evaluate agent actions, ratify recurring developer preferences, and retain the exact policy revisions behind important decisions.

Laguarde runs locally for one developer or behind a team URL. Agents interact with the same server through standard MCP; humans use the dashboard and REST API.

What the prototype proves

• Four policy categories: code rules, general guardrails, project initialization recipes, and PR review guidelines.

• Four decisions: allowed, limited, approval, and forbidden.

• Context-specific policy bundles with immutable revision identifiers.

• One local daemon with a persistent registry of projects and Git origins.

• Fail-safe action evaluation: an unmatched action is limited, not silently allowed.

• Human approval for dependency, migration, deletion, and authentication actions.

• SQLite decision records plus human-readable Markdown evidence.

• Developer feedback convergence: a human may merge any proposal immediately; three observations promote it as a stronger candidate.

• A dashboard for policy CRUD, decision evaluation/review, and feedback ratification.

Related MCP server: Cordum_io

Quick start

For a local MCP installation, use Node.js 24 or newer:

npx -y --package laguarde-mcp@0.3.2 laguarde-daemon ensure
npx -y --package laguarde-mcp@0.3.2 laguarde-daemon register --cwd .

The first command reuses the healthy local daemon or starts it once. The second registers the current Git repository and prints its project-specific MCP URL. All local projects share the daemon, dashboard, SQLite database, and audit history while remaining separately identifiable.

Conceptual MCP configuration:

{
  "mcpServers": {
    "laguarde": {
      "type": "http",
      "url": "http://127.0.0.1:3000/mcp/projects/RETURNED_PROJECT_ID"
    }
  }
}

For repository development, install Bun and run:

bun install
bun run build
bun run start

Project HTTP MCP endpoints live under http://localhost:3000/mcp/projects/:projectId, and agent-facing discovery is available at http://localhost:3000/llms.txt.

Onboarding surfaces:

• human guide: http://localhost:3000/guide;

• agent self-setup contract: http://localhost:3000/install (text/plain).

To onboard a capable agent, send it the /install URL and explicitly ask it to connect Laguarde for the current project. The contract tells it how to verify the server, make a minimal native MCP configuration change, discover the tools, and load the registered project's policy bundle.

Agent policy-gate skill

Install the optional fail-closed skill from this repository with:

npx skills add https://github.com/FuturPanda/laguarde --skill laguarde-policy-gate

The skill requires a cooperative agent to load the project-bound Laguarde policy bundle, evaluate and record every material action, and stop when policy is unavailable, limited, approval-required, or forbidden. It does not replace a sandbox or host-level execution hook.

For S3/CloudFront onboarding, generate the two static upload objects with:

bun run export:onboarding

See docs/s3-onboarding.md.

MCP Registry publication is automated through GitHub Actions after a one-time DNS authentication setup. See docs/registry-publishing.md.

The daemon's first start creates ~/.laguarde/laguarde.db, seeds global policy, and adds ten policies. Set LAGUARDE_DATA_DIR, or the more specific LAGUARDE_DB_PATH and LAGUARDE_EVIDENCE_DIR, to place persistent data elsewhere.

Agent workflow

1. get_policy_bundle retrieves the current policies and their revision IDs.

2. evaluate_action previews the boundary decision for an exact intended action.

3. record_decision re-evaluates and persists that action as evidence.

4. The agent proceeds only when allowed, narrows a limited request, waits for approval, or stops when forbidden.

5. list_preference_proposals and propose_preference turn reusable developer corrections into a human review queue.

See usage instructions for tool inputs and concrete calls.

Architecture

flowchart LR
  A[Agent / IDE] -->|MCP| M[Laguarde server]
  H[Human dashboard] -->|REST| M
  M --> J[Project registry]
  M --> P[Policy evaluation]
  P --> D[(SQLite)]
  P --> E[Markdown evidence]
  F[Developer feedback] --> Q[Proposal convergence]
  Q -->|review at any time| H
  Q -.->|3 observations promote priority| Q
  H -->|ratify| R[Immutable policy revision]
  R --> D

The published CLI uses Node.js, TypeScript, Express, SQLite, and the standard MCP SDK. Bun remains the repository's development and test runner. Policy types share one revisioned model, while category-specific configuration is stored in fields.

Important enforcement boundary

MCP connectivity makes policies discoverable and decisions auditable, but it does not technically prevent an uncooperative agent from using tools outside Laguarde. Hard enforcement requires Laguarde decisions to be wired into an execution hook, command proxy, sandbox, filesystem permissions, or CI gate.

This prototype is therefore an enforceable decision service, but only an advisory boundary until the host agent or execution environment uses it as a mandatory gate.

Repository guide

• src/ — policy engine, persistence, REST API, and MCP tools.

• public/ — human dashboard.

• llms.txt — agent-facing discovery and operating contract.

• examples/ — bootstrap, control-boundary, and feedback demos.

• docs/ — installation, usage, decisions, and current limits.

• test/ — executable behavior specification.

Verification

bun test
bun run typecheck
bun run build

Next engineering milestones

1. Add authenticated organization/team/project hierarchy and explicit policy precedence.

2. Add an execution adapter that verifies approval immediately before an agent tool call.

3. Bind approvals to an exact action digest and expiry.

4. Add database migrations and production persistence adapters.

5. Add static code/diff inspection rather than relying only on declared action metadata.