compliance-agent 0.5.0

EU AI Act compliance scanner for AI projects

ComplianceAgent

Check if your AI project follows EU rules.

The EU has new rules for AI. If you're building with OpenAI, Anthropic, LangChain, or any AI framework, you need to check whether you comply. This tool gives you a fast, automated first pass — one command, about 5 seconds. It is a heuristic aid, not legal advice, and does not by itself establish compliance.

30-Second Start · What It Does · How It Works · Examples · All Commands · FAQ

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30-Second Start

# Install (isolated CLI tool)
uv tool install compliance-agent
# no uv? use:  pipx install compliance-agent

# Check your project
compliance-agent scan .

# Done. Read what it found.

What It Does (Simple Version)

1. Scans your code — finds where you use AI (OpenAI, LangChain, etc.).
2. Checks the rules — compares your code against EU AI Act requirements.
3. Tells you what's missing — shows exactly what you need to fix.
4. Gives you the code — provides copy-paste fixes for each problem.

What You'll See

When you run compliance-agent scan ., you get a boxed terminal report: a header, a summary strip, per-article coverage, findings, and the gaps to fix. Illustrative shape (real output for the bundled sample is in examples/EXPECTED_OUTPUT.md):

╭─ EU AI Act Compliance Report ──────────────────────────────╮
│   Files scanned  3                                         │
│      Risk tier   LIMITED                                   │
╰──────────────────────────────────────────── ComplianceAgent ╯

╭─ Scan Summary ─────────────────────────────────────────────╮
│    3           1            6            9                  │
│  FILES     AI SYSTEMS    FINDINGS      GAPS                 │
╰────────────────────────────────────────────────────────────╯

╭─ Compliance Gaps ──────────────────────────────────────────╮
│ ✗ MISSING  Automated logging of AI events required (Art.12) │
│   Fix: templates/art12/event_logging.py                    │
│ ✗ MISSING  AI transparency disclosure required (Art. 50)   │
│   Fix: templates/art50/transparency_notice.py              │
│ ✗ MISSING  Error handling around AI calls (Art. 15)        │
│   Fix: add try/except + fallbacks around model calls       │
╰────────────────────────────────────────────────────────────╯

Next: compliance-agent recommend . --output ./fixes

Prefer a file? scan --format pdf --output report.pdf writes a PDF, and the separate report command writes markdown or PDF to disk. For scan, --format markdown and --format json render to the terminal/stdout — pipe json to a file if you need one (see Command Reference).

Do I Need This?

Yes, if you:

• Use OpenAI, Anthropic, Google, or any AI API
• Build chatbots or AI assistants
• Use LangChain, CrewAI, AutoGen, or LangGraph
• Deploy AI in the EU or serve EU users
• Want to avoid fines (up to €15M / 3% of turnover for most obligations, and up to €35M / 7% for prohibited practices)

No, if you:

• Don't use AI in your project
• Only use AI for personal projects (not a business)
• Don't operate in, or serve users in, the EU

Installation

ComplianceAgent is a command-line tool, so the cleanest way to install it is with a tool installer that keeps it in its own isolated environment.

Recommended (isolated CLI install)

uv tool install compliance-agent

or, with pipx:

pipx install compliance-agent

No uv or pipx yet? Install one:

brew install uv        # or: brew install pipx

Alternative: pip inside a virtual environment

On modern macOS/Linux, a bare pip install into the system Python is blocked (PEP 668, "externally-managed-environment"). Use a virtual environment:

python3 -m venv .venv
source .venv/bin/activate    # Windows: .venv\Scripts\activate
pip install compliance-agent

Latest unreleased version (from GitHub)

uv tool install git+https://github.com/latreon/compliance-agent.git
# or:  pipx install git+https://github.com/latreon/compliance-agent.git

Verify it worked

compliance-agent version
# ComplianceAgent v0.5.0

Trouble installing or running? See the Troubleshooting guide.

How It Works

Step 1: Scan your code

The scanner reads your project files and looks for AI-related patterns:

• import openai — you're using OpenAI
• from langchain — you're using LangChain
• AgentExecutor() — you're running an AI agent
• client.chat.completions.create() — you're calling an AI API

Provider and framework detection uses AST parsing (not just text search): they only fire in files that actually import the library, so a comment that mentions "OpenAI" won't trigger a provider finding. Lightweight keyword patterns (e.g. chat-interface wording) additionally scan documentation and config, so a small number of findings can come from .md files.

Step 2: Classify risk

Based on what it finds, the tool assigns a risk level:

Risk Level	What It Means	Rules That Apply
MINIMAL	Basic AI usage, no user interaction	Almost none
LIMITED	AI interacts with users	Transparency rules (Art. 50)
HIGH	AI used in an Annex III domain (hiring, credit, biometrics, law enforcement, …)	Full compliance required
UNACCEPTABLE	Banned AI practices (Art. 5)	Cannot be deployed

How the tier is decided. HIGH risk under the EU AI Act comes from the use-case domain (Annex III), not from technical capability. An autonomous agent with tools is not automatically high-risk — a résumé-screening or credit-scoring system is. ComplianceAgent classifies HIGH only when it detects Annex III domain indicators, and UNACCEPTABLE only when it detects a likely Art. 5 prohibited practice (e.g. social scoring, untargeted facial scraping). Domain indicators are matched against file paths and code content (not just file names), but only for projects that actually use AI. Both are keyword-based heuristics and provisional (Art. 6(3) also exempts some narrow-purpose systems), so a match is a prompt to self-assess and consult counsel — not a legal determination. See docs/ARCHITECTURE.md for how tiers are decided.

Step 3: Check compliance

The tool checks specific articles of the EU AI Act, for example:

Article	What It Checks	When It Matters
Art. 50	"You're talking to AI" notice	Any user-facing AI
Art. 12	Logging AI conversations	Obligation for high-risk; best practice for all AI
Art. 14	Human oversight for decisions	High-risk / agentic AI
Art. 15	Error handling and robustness	Obligation for high-risk; best practice for all AI
...	see the full list	...

Step 4: Recommend fixes

For each issue found, the tool:

1. Explains what's wrong
2. Shows which rule requires the fix
3. Provides a code template you can copy
4. Tells you exactly where to put it

ISSUE: No "You're talking to AI" notice
RULE:  EU AI Act Article 50(1)
FIX:   Copy templates/art50/transparency_notice.py into your project
WHERE: Add it before your chat endpoint

Real Examples

Example 1: Simple chatbot (Limited risk)

A basic chatbot using OpenAI:

# chatbot.py
import openai

client = openai.OpenAI()

def chat(user_input):
    return client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": user_input}],
    ).choices[0].message.content

Scan result (illustrative summary — exact wording/counts come from the scan):

Risk tier: LIMITED — user-facing AI, no Annex III high-risk domain matched
Gaps:      Art. 12 (record-keeping), Art. 50 (transparency), Art. 15 (robustness)
Fix:       add a transparency notice + event logging + error handling

Example 2: LangChain agent (Higher risk)

An agent that can search the web and send emails:

# agent.py
from langchain.agents import AgentExecutor
from langchain.tools import Tool

tools = [
    Tool(name="search", func=search_web, description="Search the web"),
    Tool(name="email", func=send_email, description="Send an email"),
]

executor = AgentExecutor(agent=agent, tools=tools)

Scan result (illustrative — the tier is HIGH only if your code also matches an Annex III high-risk domain; agentic patterns on their own drive the oversight and logging gaps below):

RISK: LIMITED (no Annex III high-risk domain detected)
FRAMEWORKS: LangChain (agent, tools)
ISSUES: several, including
  1. No human oversight before tool use (Art. 14)
  2. No logging of tool calls (Art. 12)
  3. No error handling for API failures (Art. 15)
  4. No "You're talking to AI" notice (Art. 50)
FIX: Add human-in-the-loop, logging, error handling, transparency.

Note: tool access raises real Art. 14 oversight and Art. 12 logging gaps, but it does not by itself make the system HIGH risk. The tier becomes HIGH only if the agent operates in an Annex III domain (e.g. hiring, credit, biometrics) — see How the tier is decided.

Example 3: CrewAI multi-agent

A crew of agents researching and writing:

# crew.py
from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", tools=[search])
writer = Agent(role="Writer", tools=[write])

crew = Crew(
    agents=[researcher, writer],
    tasks=[Task(description="Research", agent=researcher),
           Task(description="Write", agent=writer)],
)
crew.kickoff()

Scan result (illustrative):

RISK: LIMITED (multi-agent, but no Annex III high-risk domain detected)
FRAMEWORKS: CrewAI (agent, crew, task)
ISSUES: several, including
  1. No oversight before crew execution (Art. 14)
  2. No logging of agent actions (Art. 12)
  3. No technical documentation (Art. 11)
FIX: Add an approval workflow, logging, and documentation.

A crew researching and writing is not, by itself, an Annex III high-risk use-case, so the tier stays LIMITED. Point the same crew at résumé screening or credit decisions and the tier becomes HIGH.

Example 4: Hiring tool (High risk) — real scan output

The three examples above are illustrative snippets. This one is a real, runnable project — examples/sample-hiring-tool scores job applicants (Annex III(4): employment) and is HIGH risk, not LIMITED, so every Chapter III, Section 2 obligation applies:

compliance-agent scan examples/sample-hiring-tool

Risk tier: HIGH — matched Annex III(4) "employment, workers management and
           access to self-employment" (3 keyword hits)
Gaps:      28, spanning Art. 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 26, 27, 43, 50
Fix:       compliance-agent recommend examples/sample-hiring-tool --output ./fixes
           -> 14 recommendations, one real template per article

Full real output, including every gap and finding, is in examples/sample-hiring-tool/EXPECTED_OUTPUT.md.

Example 5: Multi-framework pipeline — real scan output

Real agent projects rarely use one framework. examples/sample-multi-framework combines LangChain (chain + tool + memory), CrewAI (a researcher/writer crew), and LangGraph (the state graph orchestrating both) in one file:

compliance-agent scan examples/sample-multi-framework

Frameworks: crewai, langchain, langgraph — detected and reported separately
Gaps:       Art. 11, 12, 14 (x2), 50 — deduplicated across frameworks, not
            one redundant copy per framework

Full real output is in examples/sample-multi-framework/EXPECTED_OUTPUT.md.

More runnable examples

Example	Shows
sample-chatbot	LIMITED risk, the minimal case
sample-hiring-tool	HIGH risk (Annex III), every Chapter III obligation, all 14 fix templates
sample-multi-framework	LangChain + CrewAI + LangGraph detected and deduplicated in one project
sample-ci-cd	A copy-paste GitHub Actions workflow that gates a PR on --fail-on
Web dashboard	See Web Dashboard below — a real screenshot from compliance-agent serve

Command Reference

# Scan a folder (. means the current folder)
compliance-agent scan .

# Output types
compliance-agent scan . --format markdown   # for reading (default); raw Markdown when piped
compliance-agent scan . --format json       # for computers / CI, to stdout
compliance-agent scan . --format sarif --output results.sarif  # GitHub code scanning
compliance-agent scan . --format pdf --output report.pdf    # PDF file (-o alias)
compliance-agent scan . --format html --output dash.html    # interactive dashboard file

# Only show serious issues (info | warning | high | critical)
compliance-agent scan . --severity high

# Skip folders (repeatable)
compliance-agent scan . --exclude "tests/*" --exclude "docs/*"

# Only check matching folders (repeatable allow-list)
compliance-agent scan . --include "src/*"

# Quieter / plainer output
compliance-agent scan . --quiet             # summary only, no per-finding detail
compliance-agent scan . --no-color          # disable colored output
compliance-agent scan . --verbose           # show what is scanned + info logs
compliance-agent scan . --no-update-check   # skip the PyPI version check

# Show how to fix each problem
compliance-agent scan . --fix

# Copy fix templates into your project
compliance-agent recommend . --output ./fixes
compliance-agent recommend . --format json    # machine-readable recommendations

# Make a shareable report file
compliance-agent report . --output audit-2026.pdf
compliance-agent report . --format html --output audit-2026.html

# Compare two scans to see whether compliance improved (see below)
compliance-agent scan . --format json -o before.json
# ...make changes...
compliance-agent scan . --format json -o after.json
compliance-agent diff before.json after.json
compliance-agent diff before.json after.json --fail-on-regression  # CI gate

# Open the local web dashboard (requires the 'web' extra)
compliance-agent serve .

# For CI/CD: plain output, fail the build on serious issues
compliance-agent scan . --ci --fail-on high

# Upgrade to the latest (or a specific) version
compliance-agent upgrade
compliance-agent upgrade 0.1.2

# Show the installed version (and whether an update is available)
compliance-agent version
compliance-agent --version          # -V: quick version, then exit

Run compliance-agent scan --help to see every option explained.

Staying up to date. After a scan, ComplianceAgent tells you if a newer version is on PyPI, then compliance-agent upgrade updates it in place (auto-detecting whether you installed with uv, pipx, or pip). The check is cached for a day, never blocks a scan, and is skipped in CI and JSON output. Disable it with --no-update-check, COMPLIANCE_AGENT_NO_UPDATE_CHECK=1, or the conventional NO_UPDATE_NOTIFIER=1.

Exit codes: 0 success · 1 --fail-on threshold met · 2 usage error.

What gets scanned. .gitignore is honored automatically, and vendored directories (node_modules, .venv, dist, build, caches, …) are always skipped. Only .py, .yaml, .yml, .json, .toml, and .md files are read; other file types are ignored. Files larger than 1 MB are skipped for speed.

JSON output is a versioned envelope — safe to parse in CI:

{
  "schema_version": "1.0",
  "tool_name": "ComplianceAgent",
  "tool_version": "0.5.0",
  "disclaimer": "This tool performs automated, heuristic technical analysis — not legal advice — ...",
  "scan_result": { "files_scanned": 3, "risk_tier": "limited", "findings": [{ "id": "...", "severity": "warning", "category": "..." }] }
}

SARIF output (--format sarif) is a standard SARIF 2.1.0 log: findings map to per-file results at their detected line, compliance gaps map to project-level results, and severities carry GitHub security-severity scores — see CI/CD Integration for wiring it into the GitHub Security tab.

Project config file (compliance.yaml)

Stop re-typing flags: declare your AI posture and scan defaults once, in a compliance.yaml (or .compliance.yaml) at the project root. Every command (scan, recommend, report, serve) picks it up automatically.

# compliance.yaml
version: 1

posture:
  # Declared EU AI Act risk tier for this project. A declaration can only
  # RAISE the detected tier (unlocking the high-risk article checks) — it can
  # never lower it, so a config file cannot manufacture false assurance.
  risk_tier: high
  intended_purpose: "CV screening assistant for the recruiting team"

scan:
  exclude:            # same syntax as --exclude
    - "docs/*"
    - "notebooks/*"
  include: []         # same syntax as --include
  fail_on: high       # same as --fail-on (CI gate)
  severity: warning   # same as --severity (display filter)
  format: markdown    # default --format
  output: null        # default --output

Rules of precedence:

• Explicit CLI flags always win over the config file.
• A broken config is a hard error (exit code 2), never silently ignored — a typo in fail_on must not quietly disable a CI gate.
• posture.risk_tier participates in classification: declaring high on a project the heuristics see as limited raises the tier (with the reason recorded in the report); declaring minimal on a project detected as high changes nothing except a note that the higher tier applies.

What It Detects

AI providers

• OpenAI (GPT-4, GPT-4o, o1)
• Anthropic (Claude)
• Google (Gemini)
• Mistral, Cohere, Groq, Together AI, Replicate, Hugging Face
• AWS Bedrock
• DeepSeek, Fireworks AI, xAI (Grok)
• Local models (Ollama, vLLM, transformers, llama.cpp, torch)

Agent patterns

• MCP servers and tool definitions
• Tool calls and function calling
• Multi-agent orchestration (CrewAI, AutoGen, LangGraph)
• Prompt templates and system prompts

Framework-aware detection

Beyond generic provider detection, dedicated detectors understand what each framework construct means for compliance (only in files that actually import the framework — AST-verified):

Framework	Detection	Compliance Mapping
LangChain	Agents, tools, memory, chains	Art. 14 (oversight), Art. 12 (logging), Art. 11 (docs)
CrewAI	Crews, agents, tasks, processes	Art. 14 (oversight), Art. 12 (logging), Art. 11 (docs)
AutoGen	Agents, group chat, function/code execution	Art. 14 (oversight), Art. 12 (logging)
LangGraph	State graphs, conditional edges, tool nodes, checkpoints	Art. 12 (logging), Art. 11 (docs), Art. 14 (oversight)
LlamaIndex	RAG indexing, retrieval/query pipelines, agents (Python + JS/TS)	Art. 10 (data governance), Art. 15 (robustness), Art. 14 (oversight)
Vercel AI SDK	Generation, tools, agent loops, structured output	Art. 50 (disclosure), Art. 9 (risk), Art. 14 (oversight), Art. 11 (docs)
Semantic Kernel	Agents/group chat, kernel setup, plugins	Art. 14 (oversight), Art. 11 (docs), Art. 9 (risk)
Haystack	Agents, pipelines, document indexing, retrieval	Art. 14 (oversight), Art. 11 (docs), Art. 10 (data governance), Art. 15 (robustness)
DSPy	ReAct agents, modules/signatures, prompt optimizers	Art. 14 (oversight), Art. 11 (docs), Art. 15 (robustness)
Instructor	Structured-output extraction	Art. 15 (robustness)

Detectors and the article probes both scan Python and JavaScript/TypeScript, so a control that lives only in TS — an AI-disclosure banner, a kill switch, a human-in-the-loop gate in a Next.js app — is found by the Art. 14/15/26/50 checks, not just Python ones. Probe terms match across snake_case, camelCase, and kebab-case, so human_in_the_loop also matches humanInTheLoop.

Compliance Coverage

ComplianceAgent checks the following EU AI Act articles and reports a per-article status (Met / Partial / Unverified / Missing / Not assessed). "Not assessed" means the article was gated out by heuristic detection (e.g. the tier stayed LIMITED) — it is not a finding that the obligation does not apply, so verify those articles manually. A requirement is Met only when a verifiable signal is found — a code construct or a concrete artifact file (comments are stripped before matching, so a # TODO note can't satisfy a requirement). An obligation merely named in documentation prose or a code comment, with no implementing mechanism, is reported as Unverified ("verify manually"), never as compliant. Matching is still heuristic — a token appearing in unrelated code can over-credit a requirement — so treat Met as "signal found, verify manually", not proof of compliance:

Article	Title	When Applicable
5	Prohibited practices	Prohibited-practice indicators (UNACCEPTABLE tier)
6	High-risk definition	High-risk tier
9	Risk management	High-risk tier
10	Data governance	Data processing or high-risk tier
11	Technical documentation	High-risk obligation; flagged as best practice for any AI usage
12	Record-keeping	High-risk obligation; flagged as best practice for any AI usage
13	Transparency to deployers	High-risk tier only
14	Human oversight	Agentic patterns or high-risk tier
15	Accuracy, robustness, cybersecurity	High-risk tier only
16	Provider obligations	High-risk tier
17	Quality management system	High-risk tier
24	Distributor obligations	Deployment artifacts present
26	Deployer obligations	High-risk tier
27	Fundamental rights impact assessment	High-risk tier (narrower scope: public bodies, credit/insurance)
43	Conformity assessment	High-risk tier
50	User transparency	User-facing AI
53-55	GPAI model provider obligations	Model training/fine-tuning signals, or a self-declared GPAI/foundation-model provider

All 17 articles above have a working fix template — see Fix Templates below.

Fix Templates

ComplianceAgent doesn't just find problems — it ships solutions. Every gap maps to a real, copy-pasteable template (index):

Article	Template	Purpose
5	prohibited_practice_escalation.py	Deployment-blocking gate + legal-clearance record
6	intended_purpose_classification.py	Intended-purpose and Annex III classification record
9	risk_management.py	Risk register and review cycle
10	data_governance.py	Dataset provenance cards
11	technical_documentation.py	Annex IV technical documentation generator
12	event_logging.py	AI event logging with retention + cleanup
13	instructions_for_use.py	Instructions-for-use generator (purpose, accuracy, limitations)
14	human_oversight.py	Human-in-the-loop checkpoints with audit trail
15	robustness_and_security.py	Guarded-call decorator, rate limiter, input validation, accuracy log
16	provider_obligations_checklist.py	Checklist verifying the Art. 9/11/12/17/72/73 artifacts a provider needs
17	quality_management_system.py	Quality management system documentation generator
24	distributor_verification.py	Distributor pre-shipment verification + non-conformance reporting
26	deployer_obligations.py	Deployer oversight staffing, incident reporting, decision notices
27	fria.py	Fundamental rights impact assessment generator
43	conformity_assessment.py	Conformity assessment record + EU database registration record
50	transparency_notice.py + content_marking.py + deepfake_disclosure.py + biometric_emotion_disclosure.py	AI disclosure, content marking, deepfake labeling, emotion-recognition/biometric-categorisation notice
53-55	gpai_model_documentation.py	GPAI model provider checklist: technical docs, model card, training-data summary, copyright policy, systemic-risk evaluation

Each template is fully working Python (compile-checked in CI), well-commented, and framework-agnostic (FastAPI, Flask, Streamlit). Full index with descriptions: templates/README.md.

PDF Reports

Generate an audit-ready PDF for compliance teams, legal, or auditors:

compliance-agent scan . --format pdf
# Report saved to: compliance-report-myproject.pdf

# Or the dedicated report command (PDF, Markdown, or HTML, custom path)
compliance-agent report . --output audit-2026.pdf

The PDF includes a cover page, an executive summary with a risk-tier badge and metrics, a risk assessment with deadlines, a color-coded findings table, compliance gaps with remediation steps, fix recommendations with code snippets, and an EU AI Act reference appendix.

PDF generation uses WeasyPrint, which needs the pango native libraries: brew install pango (macOS) or apt install libpango-1.0-0 libpangoft2-1.0-0 (Debian/Ubuntu). On macOS the Homebrew library path is detected automatically — no DYLD_FALLBACK_LIBRARY_PATH export needed. Markdown and JSON formats work without any native libraries.

Web Dashboard

Two ways to get an interactive view of your compliance posture:

Self-contained HTML file — no install beyond the CLI, works offline, safe to attach to a ticket or email:

compliance-agent scan . --format html --output dashboard.html
open dashboard.html

One file, zero external assets. Filter findings by severity or text, expand gaps for remediation steps, toggle light/dark. The full JSON envelope is embedded inside, so the file doubles as a machine-readable record.

Local dashboard server — scan on demand and track history across scans:

uv tool install 'compliance-agent[web]'   # or: pip install 'compliance-agent[web]'
compliance-agent serve .
# Open: http://127.0.0.1:8420/

The serve command opens a browser with the same dashboard plus a Run scan button and a per-project scan history (kept locally under your user data directory, capped at 50 entries) with a gap-count trend line. The server binds to localhost only, is scoped to the one project directory you launched it for, and exposes no other filesystem access.

Compare scans: select any scan in the history and hit Compare with previous scan to see a diff against the one before it — whether the risk tier improved or regressed, which compliance gaps were resolved or newly introduced, and how the finding and requirements-met counts moved. The same comparison is available on the CLI (compliance-agent diff before.json after.json, with --fail-on-regression to block a CI build that makes compliance worse) using JSON reports from scan --format json.

API docs: the dashboard's REST API is documented via OpenAPI. Follow the rail's API docs link (or open /docs for Swagger UI, /redoc for ReDoc) and fetch the machine-readable spec at /openapi.json to integrate other tools. The API is read-only over localhost; POST /api/scan still requires the dashboard's X-Compliance-Dashboard: 1 header.

Export from the dashboard: the rail's Export buttons download the scan you are currently viewing as a self-contained HTML dashboard file or an audit-ready PDF — no round-trip through the CLI needed. (PDF export uses WeasyPrint; if its native libraries are missing the dashboard tells you what to install.)

Everything the terminal report insists on carries over: MINIMAL renders in neutral teal (never green), "Not assessed" is explicitly distinguished from "not applicable", confidence is labeled as a heuristic estimate, and the disclaimer is always in view.

Real screenshot, compliance-agent serve examples/sample-hiring-tool:

MCP Server

MCP lets AI assistants like Claude call ComplianceAgent's pipeline directly as tools instead of shelling out to the CLI.

Install:

pip install 'compliance-agent[mcp]'

Run:

compliance-agent-mcp          # stdio transport — for Claude Desktop, Cursor, etc.
compliance-agent-mcp --http   # HTTP transport — for remote access (default port 8000)

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "compliance-agent": {
      "command": "compliance-agent-mcp"
    }
  }
}

Cursor — add to .cursor/mcp.json:

{
  "mcpServers": {
    "compliance-agent": {
      "command": "compliance-agent-mcp"
    }
  }
}

Works with any MCP-compatible client, not just these two.

Use absolute paths for reliable results — a relative path (e.g. ".") resolves against the MCP server process's working directory, not the project you have open in your editor.

Available tools:

Tool	Description
scan_project	Full compliance scan — markdown/JSON returned inline, or PDF/HTML/markdown/JSON written to a file via output
get_summary	Lightweight summary — files scanned, risk tier, finding counts
recommend_fixes	Fix recommendations with copy-paste templates and steps; pass output_dir to write the actual template files + RECOMMENDATIONS.md into your project
diff_scans	Compare two JSON scan reports for tier/gap/finding changes — markdown or JSON, inline or written to a file
get_article_info	Look up rules and templates for a specific EU AI Act article
list_templates	List all available fix templates, grouped by article
get_version	Report the installed ComplianceAgent version

CI/CD Integration

A runnable, copy-paste GitHub Actions workflow (with its own README covering --fail-on thresholds and exit codes) lives in examples/sample-ci-cd. The short version:

Maintainers: publishing to PyPI and listing the Action on the GitHub Marketplace each need a one-time manual setup — see RELEASING.md.

GitHub Action (recommended) — one step scans, gates the build, and writes SARIF; upload it and every finding/gap appears in your repo's Security tab:

permissions:
  security-events: write   # needed by upload-sarif
  contents: read

steps:
  - uses: actions/checkout@v4

  - name: EU AI Act Compliance Scan
    id: compliance
    uses: latreon/compliance-agent@v0
    with:
      path: .            # scan the repo root (or your AI subfolder)
      fail-on: high      # fail the build on statutory gaps and above
      # format: sarif                        (default)
      # output: compliance-results.sarif     (default)

  - name: Upload results to GitHub code scanning
    if: always()         # upload even when the gate fails the build
    uses: github/codeql-action/upload-sarif@v3
    with:
      sarif_file: ${{ steps.compliance.outputs.report }}

Plain workflow step (full control):

- name: EU AI Act Compliance Check
  run: |
    pip install compliance-agent
    compliance-agent scan . --ci --fail-on high

Pre-commit hook

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/latreon/compliance-agent
    rev: v0.5.0
    hooks:
      - id: compliance-agent-scan
        args: [--fail-on, high]

Common Questions

Is this legal advice? No. It's a technical tool that checks your code. Consult a lawyer for legal advice.

Will this slow down my CI/CD? No. It takes about 5 seconds on most projects.

What if I'm not in the EU? If you serve EU users, you still need to comply. The EU AI Act applies to anyone providing AI to EU residents.

What if I find issues? The tool gives you exact code fixes. Copy the templates into your project and re-run the scan.

Can I use this in production? Yes. Add it to your CI/CD pipeline to catch issues automatically.

Troubleshooting

Common problems and fixes are in the Troubleshooting guide. Quick hits:

• command not found: compliance-agent → run python -m compliance_agent scan .
• PDF generation fails → brew install pango (macOS), or just use --format markdown / --format json
• Too many findings → --exclude "tests/*" or --severity high

Development

git clone https://github.com/latreon/compliance-agent.git
cd compliance-agent
uv sync
uv run pytest                     # tests with coverage
uv run compliance-agent scan .    # dogfood: scan this repo

Contributing

Contributions welcome! See CONTRIBUTING.md (dev setup, tests, architecture, release process) and our Code of Conduct. Release history is in CHANGELOG.md; report vulnerabilities via SECURITY.md. How it works internally: docs/ARCHITECTURE.md.

Priority areas:

• New framework detectors (e.g. PydanticAI, OpenAI Agents SDK, Mastra — not yet covered; see docs/ARCHITECTURE.md for the current list)
• Additional templates for other articles
• Documentation improvements

Roadmap

• PyPI release
• GitHub Action on the Marketplace (action.yml — see CI/CD Integration)
• Project config file (compliance.yaml) for declared posture and scan defaults
• SARIF output for GitHub code scanning integration (--format sarif)
• JS/TS project scanning

Resources

• EU AI Act (Regulation (EU) 2024/1689) — full text
• EU AI Act explorer

License

MIT License — see LICENSE.

Disclaimer

This tool provides technical analysis, not legal advice. Consult qualified legal counsel for EU AI Act compliance decisions.