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Runtime governance for AI agents — deterministic enforcement before execution, MACI role separation, tamper-evident audit trails, and operator intervention workflows.

Project description

ACGS-Lite: Constitutional AI Governance for Agents

PyPI Python License: Apache-2.0 CI Coverage Documentation GitHub stars GitHub forks Featured in Awesome LLM Security

ACGS_Lite

The missing safety layer between your LLM and production.

'acgs-lite' is a deterministic governance engine for AI agents. Define rules in YAML, enforce them at runtime with MACI role separation, and prove compliance with tamper-evident audit trails. Every action is validated before it executes, violations are blocked, not just logged.

Star this repo if you want more open-source infrastructure for governed, production-safe agents. Early stars materially help discovery.

❤️ Community favorites

If you found ACGS-Lite through Awesome LLM Security, these are the most shared starting points:

  • Fastest proofexamples/basic_governance/ shows safe requests passing and unsafe ones blocked before execution
  • Best audit demoexamples/audit_trail/ shows the tamper-evident decision chain
  • Favorite infrastructure pathexamples/mcp_agent_client.py runs governance as shared MCP-compatible infrastructure
  • Favorite compliance proofacgs assess --framework eu-ai-act maps controls to real regulatory requirements

Hero demo

20-second proof: safe actions pass, unsafe actions get blocked before execution.

  • run python examples/basic_governance/main.py
  • watch a safe request pass
  • watch harmful and PII-like requests get blocked

Start here in 3 minutes

Fastest proof path:

  1. Block an unsafe action with examples/basic_governance/
  2. Inspect the audit evidence with examples/audit_trail/
  3. Run governance as shared infrastructure with examples/mcp_agent_client.py
pip install acgs-lite
python examples/basic_governance/main.py

Expected result includes these three outcomes near the top:

✅  Allowed:  Response to: What is the capital of France?
🚫  Blocked:  no-harmful-content — Block requests containing harmful keywords
🚫  PII gate: no-pii — Prevent PII leakage in requests

If you want the full example path, go to examples/README.md.


What this proves

  • Block before execution: unsafe actions are denied before your agent runs them
  • Separate powers with MACI: proposer, validator, executor do not collapse into one actor
  • Keep audit evidence: each decision can be chained, inspected, and verified later

🚀 5-Line Quickstart

from acgs_lite import Constitution, GovernedAgent

constitution = Constitution.from_yaml("constitution.yaml")
agent = GovernedAgent(my_llm_agent, constitution=constitution)
result = agent.run("Process this high-risk transaction")

Rules in YAML (constitution.yaml):

constitutional_hash: "608508a9bd224290"
rules:
  - id: no-pii
    pattern: "SSN|social security|passport number"
    severity: CRITICAL
    description: Block PII exposure

  - id: no-destructive
    pattern: "delete|drop table|rm -rf"
    severity: HIGH
    description: Block destructive operations

  - id: require-approval
    pattern: "transfer|payment|wire"
    severity: HIGH
    description: Financial actions require human approval

📦 Installation

pip install acgs-lite

With framework integrations:

pip install "acgs-lite[openai]"       # OpenAI
pip install "acgs-lite[anthropic]"    # Anthropic Claude
pip install "acgs-lite[langchain]"    # LangChain / LangGraph
pip install "acgs-lite[mcp]"          # Model Context Protocol server
pip install "acgs-lite[autogen]"      # AutoGen / AG2
pip install "acgs-lite[a2a]"          # Google A2A protocol
pip install "acgs-lite[agno]"         # Agno agent framework
pip install "acgs-lite[server]"       # FastAPI lifecycle HTTP server
pip install "acgs-lite[all]"          # All integrations

🛡️ Core Concepts

Governance Engine

The GovernanceEngine sits between your agent and its tools. Every action passes through it before execution. Matching rules block or flag the action; the result is an immutable ValidationResult.

from acgs_lite import Constitution, GovernanceEngine, Rule, Severity

constitution = Constitution.from_rules([
    Rule(id="no-pii", pattern=r"SSN|\bpassport\b", severity=Severity.CRITICAL),
    Rule(id="no-delete", pattern=r"\bdelete\b|\bdrop\b", severity=Severity.HIGH),
])

engine = GovernanceEngine(constitution)
result = engine.validate("summarize the quarterly report", agent_id="analyst-01")

if not result.valid:
    for v in result.violations:
        print(f"[{v.severity}] {v.rule_id}: {v.description}")

MACI — Separation of Powers

MACI prevents a single agent from proposing, validating, and executing the same action:

from acgs_lite import MACIEnforcer, MACIRole

enforcer = MACIEnforcer()

# Assign roles
enforcer.assign(agent_id="planner",   role=MACIRole.PROPOSER)
enforcer.assign(agent_id="reviewer",  role=MACIRole.VALIDATOR)
enforcer.assign(agent_id="executor",  role=MACIRole.EXECUTOR)

# Proposer creates; Validator checks; Executor runs — never the same agent
proposal = enforcer.propose("planner", action="deploy v2.1 to production")
approval = enforcer.validate("reviewer", proposal)
enforcer.execute("executor", approval)

Tamper-Evident Audit Trail

Every governance decision is written to an append-only, SHA-256-chained log:

from acgs_lite import AuditLog

log = AuditLog()
engine = GovernanceEngine(constitution, audit_log=log)

engine.validate("send email to user@example.com", agent_id="mailer")

for entry in log.entries():
    print(entry.id, entry.valid, entry.constitutional_hash)

# Verify chain integrity
assert log.verify_chain(), "Audit log tampered!"

GovernedAgent — Drop-in Wrapper

from acgs_lite import Constitution, GovernedAgent

@GovernedAgent.decorate(constitution=constitution, agent_id="summarizer")
def summarize(text: str) -> str:
    return my_llm.complete(f"Summarize: {text}")

# Raises ConstitutionalViolationError if text contains violations
result = summarize("Q4 revenue was $4.2M")

🔒 Safety Defaults

acgs-lite is fail-closed by default. This is a design principle, not a configuration option.

Guarantee Behavior
Engine exception Validation raises ConstitutionalViolationError; the action is blocked, not silently passed
Missing constitution Engine refuses to initialize; no degraded-mode passthrough
Rule match Action is blocked unless the rule explicitly sets workflow_action: warn
Audit write failure Logged at warning level; does not unblock the action
MACI misconfiguration Warning raised at startup; enforcement is advisory unless enforce_maci=True
MCP server strict-mode engine.strict is restored in try/finally at every call site — an exception during validate() cannot leave strict mode permanently disabled (as of 2.9.0)

Note: The strict-mode restoration guarantee above is scoped to the MCP server integration. Other integrations that mutate engine.strict directly (e.g., custom adapters) are responsible for their own restoration. Use engine.non_strict() (a context manager at acgs_lite.engine.core) for safe per-call non-strict validation that always restores strict mode.

To opt into fail-open (e.g., for testing), you must set it explicitly:

engine = GovernanceEngine(constitution, strict=False)  # explicit; off by default

Enforcement actions progress from least to most restrictive: warnblockblock_and_notifyrequire_human_reviewescalate_to_seniorhalt_and_alert


🗺️ Component Stability

Not all layers are equally hardened. Use this table to calibrate trust in each area:

Component Status Notes
GovernanceEngine — rule validation Stable Core hot path; Aho-Corasick matcher, fail-closed exceptions
Constitution — YAML loading, rule parsing Stable Hash-pinned; schema-validated
Rule, Severity, ValidationResult Stable Stable data model; additive changes only
MACIEnforcer — role separation Stable Role checks are enforced; pass enforce_maci=True for hard failures
AuditLog — SHA-256 chained trail Stable Thread-safe append-only; chain verification tested
GovernedAgent — drop-in wrapper Stable Synchronous and async paths covered
OpenAI / Anthropic / LangChain adapters Stable Thin validated wrappers; covers completions and streaming
Constitution lifecycle API (HTTP) 🔶 Beta Draft/review/activate/rollback endpoints are functional; API may evolve
SQLite bundle store, lifecycle persistence 🔶 Beta WAL-mode; covers single-node; multi-writer not yet hardened
acgs assess compliance mapping 🔶 Beta 18-framework coverage; control mappings improve with each release
MCP server integration 🔶 Beta Single-node; production use requires your own transport hardening
Intervention / quarantine / halt workflow 🔶 Beta Full path functional; thread-safety hardened; API may evolve
Z3 constraint verifier 🧪 Experimental Useful for high-risk scenarios; requires separate Z3 install
Lean 4 / Leanstral proof certificates 🧪 Experimental Requires mistralai extra and external Lean kernel
Newer framework adapters (Agno, A2A, LiteLLM, Mistral) 🧪 Experimental Community-contributed; test coverage varies

🌐 Integrations

OpenAI

from acgs_lite.integrations.openai import GovernedOpenAI
from openai import OpenAI

client = GovernedOpenAI(OpenAI(), constitution=constitution)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Analyze the contract"}],
)

Anthropic Claude

from acgs_lite.integrations.anthropic import GovernedAnthropic
import anthropic

client = GovernedAnthropic(anthropic.Anthropic(), constitution=constitution)
message = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Review this code"}],
)

LangChain

from acgs_lite.integrations.langchain import GovernanceRunnable
from langchain_openai import ChatOpenAI

governed_llm = GovernanceRunnable(
    ChatOpenAI(model="gpt-4o"),
    constitution=constitution,
)
result = governed_llm.invoke("Translate this document")

MCP Server

Start a governance server that any MCP-compatible agent can query:

acgs serve --host 0.0.0.0 --port 8080
from acgs_lite.integrations.mcp_server import create_mcp_server
app = create_mcp_server(constitution=constitution)

📋 Compliance Coverage

ACGS maps governance controls to 18 regulatory frameworks. Run acgs assess to generate a compliance report:

acgs assess --framework eu-ai-act --output report.pdf
Framework Coverage Key Controls
EU AI Act (High-Risk) Art. 9, 10, 13, 14, 17 Risk management, human oversight, transparency
NIST AI RMF 7 / 16 functions Govern, Map, Measure, Manage
SOC 2 + AI 10 / 16 criteria CC6, CC7, CC9 trust service criteria
HIPAA + AI 9 / 15 safeguards PHI detection, access controls, audit controls
GDPR Art. 22 10 / 12 requirements Automated decision-making, right to explanation
CCPA / CPRA 8 / 10 rights Opt-out, data minimisation, transparency
ISO 42001 Clause 6, 8, 9, 10 AI management system controls
OWASP LLM Top 10 9 / 10 risks Prompt injection, insecure output, data poisoning

🔬 Advanced: Formal Verification

For the highest-risk scenarios, ACGS supports mathematical proof of safety properties.

Z3 SMT Solver

from acgs_lite.integrations.z3_verifier import Z3ConstraintVerifier

verifier = Z3ConstraintVerifier()
result = verifier.verify(
    action="transfer $50,000 to external account",
    constraints=["amount <= 10000", "recipient in approved_list"],
)
print(result.satisfiable, result.counterexample)

Lean 4 Proof Certificates (Leanstral)

from acgs_lite import LeanstralVerifier

verifier = LeanstralVerifier()  # requires mistralai extra
certificate = await verifier.verify(
    property="∀ action : Action, action.amount ≤ 10000",
    context={"action": "transfer $5,000"},
)
print(certificate.kernel_verified)  # True only if Lean kernel accepted proof
print(certificate.to_audit_dict())  # attach to AuditEntry

⚡ Performance

Operation Latency Notes
Rule validation (Python) < 1 ms Aho-Corasick multi-pattern
Rule validation (Rust) ~560 ns Optional Rust extension
Engine batch (100 rules) ~2 ms Parallel severity evaluation
Audit write (JSONL) ~50 µs Append-only, SHA-256 chained
Compliance report < 500 ms 18 frameworks, cached

🖥️ CLI

# Validate a single action
acgs validate "send email to user@corp.com" --constitution rules.yaml

# Run governance status check
acgs status

# Generate compliance report
acgs assess --framework hipaa --output hipaa_report.pdf

# Audit log inspection
acgs audit --tail 20
acgs audit --verify-chain

# Start MCP governance server
acgs serve --port 8080

# EU AI Act Art. 14(3) kill switch
acgs halt --agent-id agent-01 --reason "anomalous behaviour detected"
acgs resume --agent-id agent-01

📖 Documentation

Guide Description
Examples Canonical demo path: block, audit, then MCP
Quickstart Up and running in 5 minutes
Architecture Engine internals, MACI deep dive
Integrations OpenAI, Anthropic, LangChain, MCP, A2A
Compliance 18-framework regulatory mapping
CLI Reference Full command reference
Why Governance? The case for deterministic guardrails
OWASP LLM Top 10 ACGS coverage of each risk
Testing Guide Testing governed agents
Constitution Lifecycle API HTTP endpoints for draft, review, eval, activation, rollback, and reject

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

git clone https://github.com/dislovelhl/acgs-lite
cd acgs-lite/packages/acgs-lite
pip install -e ".[dev]"
pytest tests/ --import-mode=importlib

📄 License

Apache-2.0. See LICENSE for details.

Commercial enterprise licences (SLA, support, air-gapped deployment) available at acgs.ai.


Constitutional Hash: 608508a9bd224290 — embedded in every validation path.

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