ComplyEdge

Runtime compliance enforcement for AI agents. Not a scanner: runs in production, on every request.

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).

Article 5 is already law. GPAI obligations carry fines from 2 August 2026. Your AI is either compliant right now, or it isn't.

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 three 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

Server source: sdks/python/complyedge/mcp_server.py. Full MCP docs: sdks/python/README.md. This MCP path uses the offline TrustLint engine; it does not call the hosted OPA/Rego policy API.

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.

Each trust page is generated from that project's real audit trail: enforcement status, check volume, and the EU AI Act articles enforced at runtime. (GitHub proxies and caches images, so the seal above can lag; the trust page 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: with article citation, rule ID, and timestamp on every decision.

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 maps to the rule corpus: EU evaluates against EU AI Act Article 5, Article 50, and GPAI obligations. US evaluates against HIPAA, SOX, COPPA, TCPA, BIPA.

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/    64 YAML rules (EU AI Act, GDPR, HIPAA, SOX, PCI DSS, and more)
rules/rego/           63 leaf OPA/Rego policies + 6 package aggregators
rules/schemas/        Rule validation schema
examples/             Usage examples (decorators, OpenAI Agents)
scripts/benchmark/    Runtime benchmark (runner + prompt YAMLs + committed results)
tests/                Rule validation + acceptance tests

Rules

64 YAML rules + 63 deterministic leaf OPA/Rego policies (+ 6 package aggregators) across 4 jurisdictions:

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): 63 leaf OPA/Rego policies (+ 6 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 on every decision. (TrustLint applies the same regex corpus offline for CI use.)

Layer 2, Interpretive (synchronous, opt-in): When called with use_semantic_fallback=True, an LLM evaluates the request and blocks if a violation is found. Off by default since v0.2.2. Adds 2–5s latency per request.

Security products protect AI from bad actors. ComplyEdge blocks EU AI Act violations at runtime: and logs a cited record on every decision.

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