Install once, get governed AI.
PII redacted · Injections blocked · Loops broken · Actions audited · EU AI Act tracked

     

     

 

     

---

See it in action

Scaffold a project and boot the governed proxy + dashboard — no Docker:

Wrap any model in a few lines — PII is stripped before the model ever sees it:

The governance dashboard, before and after simulated traffic — Admina Score 40 → 60:

First boot — Admina Score 40/100

After python scripts/simulate.py — 60/100

---

Why Admina?

	Plain LLM / RAG app	With Admina
PII in prompts/responses	leaks unless you build redaction	Redacted by default — email, SSN, IBAN, phone, IP, names
Prompt injections	reach the model	Blocked at the proxy — 15 regex + Rust heuristic scoring
Agent tool calls	unaudited	Validated pre-action + logged post-action (forensic chain)
Loop / runaway agents	burn tokens / budget	Broken — TF-IDF cosine similarity over the action stream
EU AI Act readiness	manual	Gap analysis + risk classification built-in
Audit trail	logs you hope nobody deletes	SHA-256 hash chain — tamper-evident by design
Adding governance to existing code	rewrite the call sites	Zero code changes via proxy, or 3 lines via SDK
Performance overhead	unknown	~6 µs per pipeline (Rust engine), in-process or networked
License	varies	Apache 2.0, open core

Admina is decision-support and defense-in-depth, not legal advice. See Compliance scope for the full disclaimer and limitations.

---

30-second example

from admina import GovernedModel, GovernedData, GovernedAgent, ComplianceKit
from admina.plugins.builtin.adapters.ollama import OllamaAdapter
from admina.plugins.builtin.connectors.chromadb import ChromaDBConnector

# Every call is governed: PII redacted, injections blocked, audited
adapter = OllamaAdapter(host="http://localhost:11434")
model = GovernedModel(model_name="llama3.1:8b", adapter=adapter)
response = await model.ask("Summarize this document")

# Data governance: residency enforcement, PII classification
connector = ChromaDBConnector(host="localhost", port=8000)
data = GovernedData(connector=connector, residency_zone="eu")
await data.ingest(documents)

# Agent governance: validate every tool call before execution
async def my_upstream(method, params, **kw): ...  # your MCP/HTTP client
agent = GovernedAgent(upstream=my_upstream)
result = await agent.call("tools/call", {"name": "read_file", "arguments": {}})

# Compliance: EU AI Act gap analysis and risk classification
kit = ComplianceKit()
report = kit.gap_analysis(risk_category="high", current_compliance={...})

Quick Start

Install from PyPI

# Recommended for new users: SDK + proxy + dashboard.
# Lets you run `admina dev` and see the dashboard out of the box.
pip install "admina-framework[proxy]"

# Everything (proxy + NLP + telemetry). Use this if you also want
# spaCy-based NER for PII detection or OpenTelemetry export.
pip install "admina-framework[full]"
python -m spacy download en_core_web_sm   # for [full] only

# Model-adapter provider SDKs (per-provider extras):
pip install "admina-framework[openai]"      # openai>=1.0
pip install "admina-framework[ollama]"      # ollama>=0.3
pip install "admina-framework[anthropic]"   # anthropic>=0.39
pip install "admina-framework[mistral]"     # mistralai>=1.0
pip install "admina-framework[gemini]"      # google-genai>=1.0
pip install "admina-framework[bedrock]"     # boto3>=1.34 (AWS Bedrock)

# All provider SDKs at once:
pip install "admina-framework[adapters]"

# Everything (proxy + NLP + telemetry + all adapters):
pip install "admina-framework[all]"
python -m spacy download en_core_web_sm   # for [all] only

# Optional: Rust-accelerated engine (auto-detected at runtime).
# Opt-in extra — pulls in the admina-core wheel from PyPI.
pip install "admina-framework[rust]"

# Advanced: SDK only (no proxy, no dashboard, no `admina dev`).
# Use this when embedding the SDK into another service and you don't
# need the local dev server.
pip install admina-framework

The PyPI distribution name is admina-framework; the Python import name is admina (e.g. from admina import GovernedModel). This is a normal Python pattern — same as python-dateutil → import dateutil.

The Rust engine is an optional, opt-in accelerator. The default pip install admina-framework ships only the pure-Python implementation; admina-framework[rust] adds the admina-core wheel, which Admina auto-detects at runtime (falling back to pure Python if it's absent).

The default is pure Python on purpose: the Python injection firewall currently has broader detection coverage than the Rust one (it adds obfuscation-normalisation — homoglyph, leetspeak, base64, ROT13 — and a wider multilingual pattern set). Enable [rust] when per-request latency matters more than that extra coverage. See Performance for the trade-off.

Or install from source

git clone https://github.com/admina-org/admina.git
cd admina

# Recommended: proxy + dashboard + infra deps (enables `admina dev`)
pip install -e ".[proxy]"

# Everything (proxy + NLP + telemetry)
pip install -e ".[full]"

# All model-adapter SDKs
pip install -e ".[adapters]"

# Everything (proxy + NLP + telemetry + all adapters)
pip install -e ".[all]"

# CLI workflow
admina init my-project   # Scaffold a governed AI project
cd my-project            # admina dev runs from the project directory
admina dev               # Start the local proxy + dashboard

# Full stack via Docker (no [proxy] extra required)
./scripts/bootstrap-secrets.sh   # Auto-generate .env with random credentials
docker compose up --build        # Credentials printed at bootstrap

# Note: To use the OllamaAdapter, install Ollama (https://ollama.ai)
# and pull a model first: ollama pull llama3.1:8b

# Advanced: SDK only (no proxy, no dashboard)
pip install -e .
python -c "from admina import GovernedModel; print('SDK ready')"

Dashboard: http://localhost:3000 | API docs: http://localhost:8080/docs

Architecture

Admina runs in dual mode — in-process via SDK or networked via proxy — but both modes feed the same governance pipeline.

flowchart LR
    A1["your code → GovernedModel.ask()"] --> P
    A2["AI agent → POST /mcp"] --> P
    P["governance pipeline"]
    P --> U1["Ollama / OpenAI"]
    P --> U2["MCP server / LLM"]
    classDef pipe fill:#0ea5e9,stroke:#0369a1,color:#fff;
    class P pipe;

Loading

Pipeline (identical in both modes): loop-breaker → firewall → PII redaction → guards → audit → forensic chain (SHA-256) → OTEL

The 4 Governance Domains

Domain	Capabilities	Engine
Agent Security	Anti-injection firewall (15 regex + heuristic scoring), loop breaker (TF-IDF cosine similarity)	Rust + Python
Data Sovereignty	PII redaction (email, SSN, credit cards, IBAN, phone, IP), residency enforcement, data classification	Rust + spaCy NER
Compliance	EU AI Act risk classification (Art. 6) and gap analysis (Art. 9-15), forensic black box (SHA-256 hash chain), OTEL native spans	Rust + Python
AI Infrastructure	LLM engine (Ollama, OpenAI), RAG pipeline (ChromaDB), Open WebUI	Python

All governance domains operate bidirectionally — scanning both outbound requests and inbound responses.

SDK

Four governed primitives, each with async + sync interfaces:

from admina import GovernedModel, GovernedData, GovernedAgent, ComplianceKit

Primitive	Purpose	Governance applied
GovernedModel	LLM calls (Ollama, OpenAI)	PII redaction on prompts and responses, event audit
GovernedData	Data ingestion and queries	PII classification, residency enforcement, access audit
GovernedAgent	MCP/A2A agent calls	Firewall, PII, loop breaker — full proxy pipeline in-process
ComplianceKit	Regulatory compliance	EU AI Act risk classification, gap analysis, report generation

Plugin System

9 plugin interfaces, auto-discovered from plugins/builtin/ or installed via CLI:

Interface	Builtin implementations
Model Adapter	Ollama, OpenAI
Data Connector	ChromaDB, Filesystem
Governance Domain	GuardrailsAI (toxic, jailbreak, bias, PII)
Compliance Template	EU AI Act
Transport Adapter	MCP, HTTP REST
Forensic Store	Filesystem, S3-compatible (boto3 — AWS S3, MinIO, R2, …)
Auth Provider	API Key
PII Engine	spaCy + Regex
Alert Channel	Log, Webhook

admina plugin list                    # List registered plugins
admina plugin install ./my-plugin     # Install a custom plugin
admina plugin create my-domain        # Scaffold a new plugin

CLI

admina init my-project     # Scaffold project with admina.yaml + docker-compose.yml
admina dev                 # Local mode: proxy + dashboard on :3000 (no Docker)
admina dev --stack         # Docker stack: + redis + clickhouse + grafana
admina dev --with-llm      # --stack + ollama + chromadb + open-webui
admina plugin list         # List all registered plugins
admina plugin install X    # Install a plugin from path or registry
admina plugin create X     # Scaffold a new plugin from template

admina dev defaults to a single-process local mode with zero Docker dependency: one uvicorn serves the proxy API and the dashboard SPA on the same port. Use --stack for the production-like Docker compose, or --with-llm to also boot local LLM services.

Dashboard

Real-time governance dashboard on port 3000:

• Governance Score — 0-100 composite metric (data residency, audit coverage, attack rate, forensic integrity, EU AI Act compliance)
• Live Feed — streaming governance events via WebSocket
• Compliance Gaps — EU AI Act gap analysis with article-level detail
• Infrastructure Health — proxy, Redis, forensic store, ClickHouse, OTEL status

API backend: GET /api/dashboard/score, /feed, /compliance, /sovereignty, /infra, /models

Configuration

Admina uses admina.yaml as the primary config file (with .env fallback for backward compatibility):

cp admina.yaml.example admina.yaml   # Copy and customize

See admina.yaml.example for all options including domains, AI infra, plugins, dashboard, forensic storage, alert channels, and integrations.

⚖️ Compliance scope & legal disclaimer — what Admina does and does not do legally

Admina is a self-assessment and defense-in-depth tool. The EU AI Act gap-analysis and risk classification features are decision-support aids, not legal advice. They do not replace the conformity assessment required under EU AI Act Art. 43 for high-risk systems, nor the involvement of a notified body where the regulation requires one.

EU AI Act timeline (after the Omnibus VII agreement of 7 May 2026): Art. 5 prohibitions in force since 2 February 2025; GPAI obligations in force since 2 August 2025; Art. 50 transparency for synthetic content and the new NCII / synthetic-CSAM prohibition apply from 2 December 2026; Annex III high-risk obligations from 2 December 2027 (postponed from 2 Aug 2026); Annex I high-risk from 2 August 2028 (postponed from 2 Aug 2027). The full machine-readable timeline ships with Admina as admina.domains.compliance.eu_ai_act.EU_AI_ACT_DEADLINES. See MODEL_CARD.md for the full scope, limitations, and known failure modes of every Admina component.

Integrations

GuardrailsAI — ML-based content validation as a governance plugin

ML-based content validation (toxic language, jailbreak, bias, PII via Presidio) as a governance domain plugin:

# Upstream guardrails-ai is currently in PyPI quarantine. Install it
# manually from your local mirror or wheel cache; once available, the
# plugin in admina/plugins/builtin/guards/guardrailsai_guard.py will
# detect it automatically.
pip install <your-guardrails-ai-wheel>

Enable in admina.yaml under agent_security.domains.guardrailsai. All inference runs locally by default — no data leaves the deployment perimeter.

OpenClaw — govern OpenClaw agent actions via pre/post-action hooks

Govern OpenClaw agent actions through the Admina proxy. Every tool call, shell command, and API request is validated before execution:

cd integrations/openclaw/admina-governance
chmod +x setup.sh && ./setup.sh

The skill uses POST /api/v1/validate (pre-action) and POST /api/v1/audit (post-action) endpoints.

n8n — community nodes for n8n workflow automation

Node	Purpose
Admina Govern	Inline governance check — validates workflow data, blocks injections, redacts PII
Admina Audit	Logs workflow events to forensic black box with EU AI Act risk classification
Admina Dashboard	Trigger node — fires on governance events via WebSocket

Install: npm install n8n-nodes-admina in your n8n instance.

Cheshire Cat AI — govern all Cheshire Cat interactions via Python hooks

Three Python hooks (agent_fast_reply, before_cat_sends_message, before_cat_recalls_memories):

cd integrations/cheshirecat/admina-plugin
./setup.sh    # Start Admina sidecar
# Copy plugin into Cheshire Cat plugins/ directory

LangChain — drop-in callback handler

Governs every LLM call and tool invocation in-process:

from admina.integrations.langchain.callbacks import AdminaCallbackHandler

handler = AdminaCallbackHandler()
llm = ChatOpenAI(callbacks=[handler])

CrewAI — step and task callbacks for multi-agent governance

from admina.integrations.crewai.callbacks import admina_step_callback, admina_task_callback

agent = Agent(role="Researcher", step_callback=admina_step_callback)
crew = Crew(agents=[agent], tasks=[task], task_callback=admina_task_callback)

See full integration docs for details.

Performance — Hybrid Python + Rust engine

The Rust core engine is an optional accelerator. The default pip install admina-framework ships only the pure-Python implementation; enable the Rust engine with the opt-in extra pip install "admina-framework[rust]" (or build from source for local development — maturin develop --release --manifest-path core-rust/Cargo.toml, see CONTRIBUTING.md). At runtime Admina auto-detects whichever is available and falls back transparently to Python if the Rust extension is not installed.

Detection trade-off (why Rust is opt-in, not the default). The Rust firewall is faster but currently detects a narrower set of attacks than the pure-Python firewall. The Python engine normalises common evasions before matching (homoglyph, leetspeak, char-by-char hyphenation, base64, ROT13) and carries a wider multilingual pattern set; the Rust engine does not yet. On an internal 14-attack evasion corpus the Python firewall blocks all 14 while the Rust firewall blocks 7 (the plain-text and multilingual-keyword attacks), with no false positives on either side. Full Rust↔Python detection parity is tracked for 0.10. Until then, keep the default (pure Python) when detection breadth matters; opt into [rust] when latency dominates.

Measured numbers below assume the Rust engine is loaded:

Component          Rust (median)   P95        P99
-----------------  -------------   ---------  ---------
Firewall (regex)   2.08us          2.33us     2.50us
PII Scanner        0.62us          0.67us     0.71us
Loop Breaker       2.38us          2.67us     2.75us
Hash Chain         1.00us          1.12us     1.25us
-----------------  -------------   ---------  ---------
4-Domain pipeline  6.25us          7.04us     7.29us

Rust vs Python comparison (click to expand)

Component          Python (median)   Rust (median)   Speedup
-----------------  ---------------   -------------   --------
Firewall           7.79us            2.08us          3.7x
PII (regex-only)   8.21us            0.62us          13.2x
PII (with spaCy)   1 992us           0.62us          3 213x
Loop (sklearn)     505us             2.38us          212x
-----------------  ---------------   -------------   --------
Full pipeline      2 261us           5.21us          434x

Traffic Simulator

Generate realistic governance traffic to test and demo the platform:

# Start the proxy
docker compose up -d

# Default: 60s at 2 req/s
python scripts/simulate.py

# Intense: 5 minutes at 10 req/s
python scripts/simulate.py --duration 300 --rate 10

Generates a weighted mix of: clean MCP requests, injection attempts, PII content, loop triggers, REST validate/audit calls, EU AI Act classifications, and dashboard reads. Colored terminal output with per-event action and summary counters.

Infrastructure & Services

The full stack (docker compose up) runs 9 containers:

Port	Service	Description
8080	Proxy	MCP proxy + REST API + OpenAPI docs
3000	Dashboard	Real-time governance web UI
3001	Grafana	Metrics dashboards
4317	OTEL Collector	OTLP gRPC ingestion

ClickHouse and Redis are internal only (not exposed to host).

🗄️ Forensic backends (4 options) — choose deliberately

The forensic blackbox (the SHA-256 hash chain that makes the audit trail tamper-evident) supports three backends. Read this before picking one for production.

Backend	License	When to use	Caveats
memory (default)	n/a	Local development, tests, demos	Records are LOST on restart — no audit persistence. Loud warning at startup.
filesystem	n/a	Single-host on-prem, air-gapped, smaller deployments	Persistence depends on the host filesystem; not ideal for HA. Requires FORENSIC_BASE_DIR.
s3 (boto3)	Apache 2.0 (boto3)	Production / HA / multi-region	Works with any S3-compatible service — AWS S3, MinIO servers, Cloudflare R2, Backblaze B2, SeaweedFS (Apache 2.0), Garage (AGPLv3), Ceph RGW (LGPLv2). Supports WORM Object Lock.

Using MinIO? Point the s3 backend at your MinIO server via FORENSIC_S3_ENDPOINT — MinIO speaks the S3 API, so no MinIO-specific client is needed. The legacy minio-SDK backend was removed in 0.9.5 (the MinIO Python SDK is archived); FORENSIC_BACKEND=minio now transparently routes to the s3 backend with a migration warning.

⚙️ Environment variables (Docker / .env)

Variable	Default	Description
ADMINA_API_KEY	(empty)	API key for all endpoints
UPSTREAM_MCP_URL	http://localhost:9000	Default upstream MCP server
REDIS_URL	redis://localhost:6379/0	Session state + rate limiting
FORENSIC_BACKEND	memory	Forensic store: memory | filesystem | s3
LOG_LEVEL	INFO	Logging verbosity

📁 Full project structure

admina/
+-- admina/                 SDK package (GovernedModel, GovernedData, GovernedAgent, ComplianceKit)
|   +-- plugins/            Plugin base classes + registry
+-- domains/                4 governance domains
|   +-- data_sovereignty/   PII, residency, classification
|   +-- ai_infra/           LLM engine, RAG pipeline, Web UI
|   +-- agent_security/     Firewall, loop breaker, proxy
|   +-- compliance/         EU AI Act, forensic, OTEL
+-- plugins/builtin/        Reference plugin implementations
|   +-- adapters/           Ollama, OpenAI
|   +-- connectors/         ChromaDB, Filesystem
|   +-- domains/            GuardrailsAI
|   +-- compliance/         EU AI Act template
|   +-- transports/         MCP, HTTP REST
|   +-- forensic/           Filesystem
|   +-- auth/               API Key
|   +-- pii/                spaCy + Regex
|   +-- alerts/             Log, Webhook
+-- proxy/                  FastAPI proxy + Rust engine bridge
|   +-- api/                Dashboard + integration REST endpoints
+-- cli/                    CLI commands (init, dev, plugin)
+-- core/                   Config, types, event bus
+-- core-rust/              Rust governance engines (PyO3)
+-- dashboard/              Real-time governance web UI
+-- integrations/
|   +-- openclaw/           OpenClaw governance skill
|   +-- n8n/                n8n community nodes
+-- tests/                  800+ tests (pytest)
+-- docker-compose.yml      Full stack deployment (9 containers)

🔌 API examples (curl)

# Health check (always public)
curl http://localhost:8080/health

# Governance stats
curl http://localhost:8080/api/stats -H "X-API-Key: $ADMINA_API_KEY"

# Proxy an MCP call (all governance domains applied)
curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{...}}'

# Validate content (REST API for integrations)
curl -X POST http://localhost:8080/api/v1/validate \
  -H "Content-Type: application/json" \
  -d '{"content": "Check this text for governance issues"}'

# Audit an action (forensic logging)
curl -X POST http://localhost:8080/api/v1/audit \
  -H "Content-Type: application/json" \
  -d '{"event": {"action": "llm_call", "status": "success"}}'

# EU AI Act risk classification
curl -X POST http://localhost:8080/api/compliance/classify \
  -H "Content-Type: application/json" \
  -d '{"description":"AI credit scoring","use_case":"lending","data_types":["financial"]}'

# Dashboard governance score
curl http://localhost:8080/api/dashboard/score

Project documents

• CONTRIBUTING.md — development setup, testing, and pull request workflow
• MODEL_CARD.md — transparency artifact for every Admina governance component (intended use, scope, limitations, known failure modes), aligned with EU AI Act Art. 13 and NIST AI RMF
• ROADMAP.md — planned milestones from 0.9.x to 1.0 and beyond
• CHANGELOG.md — release notes
• SECURITY.md — coordinated disclosure policy
• CODE_OF_CONDUCT.md — Contributor Covenant 2.1
• Browse the AI-generated wiki → deepwiki.com/admina-org/admina

Admina is Apache 2.0. Contributions are welcome.

License

Copyright © 2025–2026 Stefano Noferi & Admina contributors

Licensed under the Apache License, Version 2.0. See LICENSE for the full text.

---

Heimdall — the Governance Owl

admina.org · Created by Stefano Noferi · Pisa, Italy