Agent Guardrail MCP Server

Agent Guardrail

Action-level governance for AI agents — control what they DO, not what they SAY.

The Problem

AI agents are getting tool access. They can run shell commands, make API calls, read files, spend money. But most "guardrails" only filter what agents say — not what they do.

Real incidents:

• AutoGPT autonomously spent $10K+ on API calls in a single session
• ChaosGPT attempted to access military systems and recruit other AI agents
• Air Canada chatbot invented a refund policy that cost the airline $800+

You need action-level control. Not output filtering.

What Agent Guardrail Does

Agent Framework --> Agent Guardrail --> {allow, deny, require_approval}
                                    --> Flight Recorder logs everything

• Policy Engine — allowlists, denylists, glob patterns for tools and targets
• Spend Caps — daily and total USD limits per agent
• Kill Switch — instantly deny all actions for a runaway agent
• Flight Recorder — every action logged with full replay capability
• Approval Gates — route risky actions to human review
• Risk Scoring — automatic risk assessment per action type
• 3 Templates — restrictive, moderate, permissive (apply in one command)
• Pay-per-eval Billing — free tier + BTC credit packs via Blockonomics

Zero dependencies. Python stdlib only. SQLite for storage.

30-Second Quickstart

pip install agent-guardrail

# Register an agent
agent-guardrail register "my-research-agent" --framework langchain

# Apply the moderate policy template
agent-guardrail apply-template moderate <agent-id>

# Test it
agent-guardrail eval <agent-id> bash --target /workspace/test.sh     # -> allow
agent-guardrail eval <agent-id> bash --target /etc/shadow             # -> deny
agent-guardrail eval <agent-id> sudo                                  # -> deny

Python API

from agent_guardrail import GuardrailStore, PolicyEngine, DEFAULT_POLICIES

# Initialize
store = GuardrailStore()  # ~/.agent-guardrail/guardrail.db
engine = PolicyEngine(store)

# Register agent
agent = store.register_agent("my-agent", framework="langchain")

# Apply policy template
store.save_policy({
    "name": "moderate",
    "agent_id": agent["id"],
    "rules": DEFAULT_POLICIES["moderate"]["rules"],
})

# Evaluate actions
decision = engine.evaluate(agent["id"], "bash", target="/workspace/run.sh")
# -> PolicyDecision(decision="allow", risk_score=0.7)

decision = engine.evaluate(agent["id"], "bash", target="/etc/shadow")
# -> PolicyDecision(decision="deny", reason="Target '/etc/shadow' is denied...")

# Evaluate + record to flight recorder
decision = engine.evaluate_and_record(
    agent_id=agent["id"],
    action_type="api_call",
    tool_name="openai_chat",
    cost_usd=0.05,
    session_id="session-123",
)

Framework Integrations

LangChain Callback

from agent_guardrail import GuardrailStore, PolicyEngine

class GuardrailCallback:
    """Drop into any LangChain agent as a callback handler."""
    def __init__(self, agent_id, db_path=None):
        self._engine = PolicyEngine(GuardrailStore(db_path=db_path))
        self.agent_id = agent_id

    def on_tool_start(self, serialized, input_str, **kwargs):
        decision = self._engine.evaluate_and_record(
            agent_id=self.agent_id,
            action_type="tool_call",
            tool_name=serialized.get("name"),
            target=input_str[:200],
        )
        if decision.decision == "deny":
            raise PermissionError(f"Guardrail: {decision.reason}")

CrewAI Task Guardrail

from agent_guardrail import GuardrailStore, PolicyEngine

def make_guardrail(agent_id, db_path=None):
    engine = PolicyEngine(GuardrailStore(db_path=db_path))

    def check(task_output):
        decision = engine.evaluate_and_record(
            agent_id=agent_id, action_type="task_output",
            target=str(task_output)[:200],
        )
        if decision.decision == "deny":
            return (False, f"Blocked: {decision.reason}")
        return (True, task_output)
    return check

# task = Task(description="...", guardrail=make_guardrail("agent-id"))

Universal Decorator

from agent_guardrail import GuardrailStore, PolicyEngine
import functools

def guardrail(agent_id, action_type="function_call", db_path=None):
    engine = PolicyEngine(GuardrailStore(db_path=db_path))
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            target = str(args[0])[:200] if args else None
            decision = engine.evaluate_and_record(
                agent_id=agent_id, action_type=action_type,
                tool_name=func.__name__, target=target,
            )
            if decision.decision == "deny":
                raise PermissionError(f"Guardrail: {decision.reason}")
            return func(*args, **kwargs)
        return wrapper
    return decorator

@guardrail("my-agent", action_type="bash")
def run_command(cmd):
    ...

Hosted API (For Agents)

The library is for humans. The API is for agents.

An orchestrator running 5 sub-agents doesn't pip install — it calls an endpoint.

# Start the proxy server
pip install agent-guardrail[proxy]
guardrail-proxy --port 8300 --admin-key YOUR_ADMIN_KEY

# Register an agent (admin)
curl -X POST http://localhost:8300/v1/agents \
  -H "X-Admin-Key: YOUR_ADMIN_KEY" \
  -d '{"name": "research-agent", "framework": "crewai"}'

# Evaluate an action (agent)
curl -X POST http://localhost:8300/v1/evaluate \
  -H "X-API-Key: gw_agent_key_here" \
  -d '{
    "agent_id": "...",
    "action_type": "bash",
    "tool_name": "shell",
    "target": "/etc/shadow",
    "cost_usd": 0.0
  }'
# -> {"decision": "deny", "reason": "Target denied...", "risk_score": 0.7}

Full API docs at http://localhost:8300/docs (Swagger UI).

Billing & Pricing

Free tier included. Pay with Bitcoin when you need more.

Credits are prepaid and never expire. Admin-authenticated requests bypass billing entirely.

How it works:

# Check your balance
curl http://localhost:8300/v1/billing/balance \
  -H "X-API-Key: gw_your_agent_key"

# Buy credits (returns a BTC address + amount)
curl -X POST http://localhost:8300/v1/billing/checkout \
  -H "X-API-Key: gw_your_agent_key" \
  -d '{"pack_id": "pack_1000"}'
# -> {"btc_address": "bc1q...", "amount_btc": 0.00015, "amount_satoshi": 15000, ...}

# Pay the BTC address -> webhook confirms -> credits granted automatically

When free tier is exhausted and no credits remain, /v1/evaluate returns 402 Payment Required with a link to available packs.

Self-hosted billing: Set BLOCKONOMICS_API_KEY and BLOCKONOMICS_WEBHOOK_SECRET environment variables. Without these, billing is disabled and all evaluations proceed without metering (backward compatible).

Policy Rules Reference

{
    "tool_allowlist": ["read_file", "write_file"],    # Only these tools allowed
    "tool_denylist": ["sudo", "rm", "delete*"],       # These tools always denied
    "target_allowlist": ["/workspace/*"],              # Only these targets allowed
    "target_denylist": ["/etc/*", "*.env", "*.key"],   # These targets always denied
    "network_allowlist": ["api.openai.com"],           # Allowed network targets
    "network_denylist": ["*"],                         # Denied network targets
    "spend_cap_daily_usd": 25.0,                      # Daily spend limit
    "spend_cap_total_usd": 500.0,                     # Lifetime spend limit
    "require_approval": ["bash", "install"],           # Human approval required
    "risk_threshold": 0.8,                             # Auto-approval gate
}

Patterns support glob matching (*, ?, [abc]).

Decision Flow

Kill switch? ──deny──> DENY
      |
Agent enabled? ──no──> DENY
      |
Spend cap? ──exceeded──> DENY
      |
Tool denylist? ──match──> DENY
      |
Target denylist? ──match──> DENY
      |
Approval required? ──match──> REQUIRE_APPROVAL
      |
Risk threshold? ──exceeded──> REQUIRE_APPROVAL
      |
Tool allowlist? ──not in list──> DENY
      |
Target allowlist? ──not in list──> DENY
      |
DEFAULT ──> ALLOW

Architecture

+-------------------+     +------------------+     +-----------------+
|  Agent Framework  |---->|  Billing Check   |---->|  Policy Engine  |
|  (LangChain,     |     |  (free tier /    |     |  (evaluate)     |
|   CrewAI, custom) |     |   credits)       |     +-----------------+
+-------------------+     +------------------+            |
                                 |                        v
                                 |           +------------------------+
                          402 if empty       |  Decision:             |
                                             |  allow / deny /        |
                                             |  require_approval      |
                                             +------------------------+
                                                         |
                                                         v
                                             +-----------------+
                                             |  Flight Recorder|
                                             |  (SQLite)       |
                                             +-----------------+

+-------------------+     +------------------+
|  BTC Payment      |---->|  Blockonomics    |
|  (checkout)       |     |  (xpub-derived   |
+-------------------+     |   addresses)     |
                          +------------------+
                                 |
                          webhook (status=2)
                                 |
                                 v
                          +------------------+
                          |  Credit Grant    |
                          |  (billing_ledger)|
                          +------------------+

Comparison

| Feature | Agent Guardrail | Guardrails AI | NeMo Guardrails | DIY | |---------|:-:|:-:|:-:|:-:| | Action-level control | Yes | No (output only) | No (dialogue only) | Manual | | Spend caps | Yes | No | No | Manual | | Kill switch | Yes | No | No | Manual | | Flight recorder | Yes | No | No | Manual | | Pay-per-eval billing | Yes (BTC) | No | No | Manual | | Zero dependencies | Yes | No (many) | No (many) | Varies | | Framework agnostic | Yes | LangChain-focused | LangChain-focused | Yes | | Hosted API | Yes | Cloud only | No | Manual |

CLI Reference

agent-guardrail agents                      # List registered agents
agent-guardrail register "name"             # Register a new agent
agent-guardrail kill <agent_id>             # Emergency kill switch
agent-guardrail unkill <agent_id>           # Revoke kill switch
agent-guardrail policies                    # List policies
agent-guardrail apply-template <template> <agent_id>
agent-guardrail actions [--agent X] [--decision deny]
agent-guardrail replay <session_id>         # Session replay
agent-guardrail approvals                   # Pending approvals
agent-guardrail approve <id>                # Approve action
agent-guardrail deny <id>                   # Deny action
agent-guardrail eval <agent_id> <type> [--target X] [--cost 0.5]
agent-guardrail stats                       # Statistics

Configuration

License

MIT