ComplyEdge

ComplyEdge checks every request before it reaches your AI and every answer before your user, blocks the ones that break a rule, and records each decision outside your AI system, with the legal citation when a rule fires. Prompt injection, Article 5 practices and, on EU checks through the hosted API, personal data and secrets (email, phone, IBAN, card and national ID numbers, API keys, private keys) are blocked; Article 50, GPAI and high-risk rules flag text that describes a missing control. The same rules are available to your agent as an MCP server that checks the text you pass it, offline, with an article citation on every finding. It checks this prompt or output; it does not classify your system.

Ships three ways: a Python SDK, an offline CI linter (TrustLint), and an MCP server, a Model Context Protocol server that exposes compliance checks as tools to any MCP host (Claude, Cursor, MCP Inspector).

Your system prompt says no and the model does it anyway. Customer data and API keys end up in the prompt. ComplyEdge blocks it before it lands. If an auditor, a regulator or a large customer ever asks what your AI blocked, you have a record of every check, not just logs.

What does your compliance tool tell a regulator when it blocks a request? A probability score?

ComplyEdge says: Article 5(1)(a), rule rego-art5-1a-001, timestamp, input hash. One is an audit trail. One is a guess.

MCP Server (Model Context Protocol)

ComplyEdge TrustLint is an MCP server built on the official MCP Python SDK (mcp>=1.9). It gives an agent article-cited compliance checks instead of a probability score, and it runs fully offline: no API key, no network call, rules evaluated from the bundled YAML corpus.

Tools (the server exposes MCP tools only: no resources, no prompts; all are read-only and idempotent):

Local (stdio): for Claude Desktop, Cursor, MCP Inspector, or any MCP host:

pip install 'complyedge[mcp]'
complyedge-mcp          # or: python -m complyedge.mcp_server

{
  "mcpServers": {
    "complyedge": {
      "command": "complyedge-mcp"
    }
  }
}

Remote (Streamable HTTP): https://mcp.complyedge.io/mcp

sandbox_check is the one tool that needs a key. Locally, set COMPLYEDGE_API_KEY in the server's environment (without it the tool is not listed and the server stays fully offline). On the hosted server it is always listed and works only for a caller that sends its own key as the Authorization: Bearer <key> header on the MCP connection; the key is never a tool argument, never logged, and the hosted server keeps no keys.

{
  "mcpServers": {
    "complyedge": {
      "url": "https://mcp.complyedge.io/mcp",
      "headers": { "Authorization": "Bearer ce_live_your_api_key" }
    }
  }
}

Server source: sdks/python/complyedge/mcp_server.py. Full MCP docs: sdks/python/README.md. The three offline tools use the TrustLint engine and never call the hosted OPA/Rego policy API; sandbox_check is the one that does, and only with your key.

Install (coding agents):

pip install complyedge
pip install trustlint
pip install 'complyedge[mcp]'
npx -y @complyedge/mcp
claude mcp add complyedge -- npx -y @complyedge/mcp

Listing: smithery.ai/servers/complyedge/complyedge

Cursor one-click: Install TrustLint MCP

OpenAI Agents extra (hosted path; needs COMPLYEDGE_API_KEY):

pip install 'complyedge[agents]'

from complyedge.agents import create_compliance_guardrail

CI: uses: complyedge/trustlint-action@v1

GOPAL is an OPA library in your process. ComplyEdge is per-request deny + citation + AI Trust Center + MCP.

Related MCP server: scan-your-ai-toolkit

Live enforcement seals

Not a static badge. These seals reflect live /v1/check traffic from open-source projects embedding ComplyEdge: they change as real enforcement happens.

Both projects below are our own. ComplyEdge runs in production against our own code before we ask anyone else to run it against theirs. You choose the region when you create an account: EU (https://eu.api.complyedge.io, key prefix ce_eu_) or US (https://api.complyedge.io, key prefix ce_). The seals below use the US host because those accounts live in the US. The SDK reads the key and calls that host. To move an existing account, use Request a region change in the dashboard. Support moves the account and issues a new key. Setting an API URL does not move the account. The region is where checks run and audit records are stored; prompt text is never stored, only its SHA-256 hash. It does not decide which laws are checked: the jurisdiction field on each check does. No general law requires either region. US law does not require US storage, and GDPR allows transfers outside the EU with safeguards such as standard contractual clauses (Chapter V). Choose the region your own contracts or customers ask for.

Each AI Trust Center is generated from that project's real audit trail: enforcement status, check volume, and the EU AI Act articles its traffic is checked against. (GitHub proxies and caches images, so the seal above can lag; the AI Trust Center is always current.)

Embed one on your own project: Enforcement Seal docs.

Quick Start

pip install complyedge

from complyedge import compliance_check

@compliance_check(jurisdiction="EU", agent_id="my-agent")
def my_agent(prompt):
    return llm.generate(prompt)  # every input and output checked

Three lines. Every AI input and output evaluated against the EU AI Act rule corpus (Article 5, Article 50, GPAI). Violations blocked before they reach the user. Every decision gets a rule ID, timestamp and text hash; every block also gets its legal citation.

Set COMPLYEDGE_API_KEY to your key. The decorator activates by default; to disable without removing the key (e.g., in CI), set COMPLYEDGE_ENABLED=false.

Without a decorator

from complyedge import is_safe, check
import os

api_key = os.environ["COMPLYEDGE_API_KEY"]

# Boolean check: returns True if no violations
if not is_safe(prompt, api_key=api_key, jurisdiction="EU"):
    raise ValueError("Prompt violates EU AI Act")

# Full result: returns ComplianceResult with the violations that blocked it
result = check(prompt, api_key=api_key, jurisdiction="EU")
if not result.allowed:
    for v in result.violations:
        print(v.rule_id, v.severity, v.rule_description)

rule_id is the citation key: every rule carries its article reference in the corpus (rego-art5-1c-001 → Article 5(1)(c)), and the full citation text ships with the rule under rules/.

jurisdiction is where the end user is, not where your company is based: the EU AI Act applies when an AI system's output is used in the EU, wherever the provider or deployer is established (Art. 2(1)(c)). Default: EU. On the hosted API's deterministic path, EU runs the EU AI Act rules, US and US-* run the SOX §302 disclosure rule, and every check runs prompt-injection detection. The other US rules (HIPAA, COPPA, TCPA, BIPA, CCPA, NYC LL144, ECPA) and the GDPR rules run offline in TrustLint, not on the hosted /v1/check path. The opt-in Layer 2 (use_semantic_fallback=True) adds two LLM judges: a prompt-injection classifier that judges by meaning against the same Article 15 categories and cites them, and a general LLM review. Neither runs the other rules.

TrustLint, Offline Linter

No API key required. Scans text against the YAML rule corpus using regex patterns. Published as a standalone package, versioned independently of the SDK.

pip install trustlint

trustlint check --text "We use social credit scoring to evaluate applicants"
# → CRITICAL: EU_AI_ACT_ART5_SOCIAL_SCORING_001, Article 5(1)(c)

Exit codes: 0 = pass, 1 = violations found. Designed for CI/CD pipelines. Source: packages/trustlint/.

Rule IDs: two namespaces

ComplyEdge resolves the same regulations through two engines, each with its own rule-ID namespace:

• Runtime API (OPA/Rego): IDs like rego-art5-1c-001: returned by compliance_check and the /v1/check API. This is the audit trail your production system logs.

• TrustLint (offline, YAML corpus): IDs like EU_AI_ACT_ART5_SOCIAL_SCORING_001: emitted by the offline linter.

Both cite the same legal article and differ only in engine. Map between them via the article reference carried in every rule.

What's In This Repo

sdks/python/          Python SDK (@compliance_check decorator, CLI)
  └ complyedge/mcp_server.py   MCP server (stdio): check_compliance, list_rules, scan_prompt
packages/trustlint/   Offline regex linter (TrustLint): no API key, for CI/CD
rules/regulations/    63 YAML rules (EU AI Act, GDPR, HIPAA, SOX, PCI DSS, and more)
rules/rego/           75 leaf OPA/Rego policies + 8 package aggregators
rules/schemas/        Rule validation schema
rules/scripts/        Schema validator (`validate_rules.py`)
examples/             Usage examples (decorators, OpenAI Agents)
scripts/benchmark/    Runtime benchmark (runner + prompt YAMLs + committed results)
tests/                Rule validation + acceptance tests

Rules

63 YAML rules + 75 deterministic leaf OPA/Rego policies (+ 8 package aggregators) across 4 jurisdictions.

What a decision from these policies establishes is not uniform, and we publish the split. Every leaf decides by matching the evaluated text (no LLM on the hot path). For 32 of them the text is the regulated act (Article 5 prohibited practices, Article 15 prompt-injection resilience, personal data and secrets matched by format and checksum, one US disclosure control) so a block prevents the act and the citation on the decision is load-bearing. The other 43 fire on text describing a state the engine cannot verify: no text matcher can establish whether a technical file, a quality management system, a human-oversight assignment or a FRIA exists. Those are useful for triage; they are not a compliance finding, and their silence is not one either. Per-rule table: docs/rules-management/corpus-evidence-classification.md.

Each rule specifies conditions, severity, detection scope, and remediation with legal citations. See the rule schema for the format.

Writing Custom Rules

id: MY_CUSTOM_RULE_001
jurisdiction: EU
effective_date: "2025-02-02"
description: "Detect prohibited practice X under Article Y"
severity: critical
conditions:
  - type: regex
    value: "prohibited pattern"
source:
  regulation: "EU AI Act"
  article: "Article Y(1)(z)"

Validate: cd rules && python scripts/validate_rules.py

Architecture

Layer 1, Deterministic (hot path): 75 leaf OPA/Rego policies (+ 8 package aggregators) evaluate every request, no LLM. The engine (OPA/Rego + TrustLint) evaluates in 4.87ms p99 in a local microbenchmark against the current 6-package bundle (layer1_latency_latest.json, best of 5 trials, 2026-08-04). End-to-end through the live API, the published 60-prompt run measured a p50 of 139ms and p95 of 2,519ms across the 39 OPA-decided prompts, with individual requests spanning 47ms to 10.7s (runtime_benchmark_latest.json, 2026-07-28). That run mixes cold and concurrent invocations against a Lambda-backed API, which is where the long tail comes from; we publish the whole run rather than a hand-picked warm figure. Opting into the Layer 2 LLM adds 2–5s on the long tail. Binary pass/block, legal citation when a rule fires. (TrustLint applies the same regex corpus offline for CI use.)

Layer 2, Interpretive (synchronous, opt-in): When called with use_semantic_fallback=True, two LLM judges run in parallel after the rules pass, and either can block: a prompt-injection classifier that judges by meaning against the Article 15 prompt_security categories (a block is llm-art15-ipi-*, carries the Article 15 citation, and must quote the injected text from the request) and a general LLM review. If either cannot answer, the request is denied. Off by default since v0.2.2. Adds 2–5s latency per request.

Security products protect AI from bad actors. ComplyEdge blocks requests and answers that break a rule, and records every decision, with the legal citation when a rule fires. The record is kept outside your AI system, so it is evidence your system did not write about itself.

Benchmark

A 60-prompt corpus runs against the live API. The runner, prompt YAMLs, and the latest result JSON are committed under scripts/benchmark/: inspect the results directly, or re-run with your own COMPLYEDGE_API_KEY.

Contributing

We welcome rule contributions. See CONTRIBUTING.md for details.

Every rule must include: article + paragraph citation, verifiable detection condition, and test cases.

Security

To report a vulnerability, see SECURITY.md. Do not open a public issue for security reports.

License

Apache License 2.0: see LICENSE.

Links

• Website: complyedge.io

• MCP server docs: sdks/python/README.md · remote endpoint https://mcp.complyedge.io/mcp

• Blog: complyedge.io/blog/

• GPAI Compliance Benchmark: complyedge.io/blog/gpai-compliance-benchmark.html

• Why OPA/Rego for EU AI Act: complyedge.io/blog/why-opa-rego-eu-ai-act.html

• PyPI: pypi.org/project/complyedge

• Changelog: CHANGELOG.md

• Rule Schema: rules/schemas/rule-schema.json