aceteam-aep 0.4.0

AEP-native execution layer for AI agents - spans, costs, budget enforcement

aceteam-aep

Trust & safety infrastructure for AI agents. Add cost tracking, safety detection, and enforcement to any LLM-powered tool — zero code changes required.

Installation

pip install aceteam-aep[all]               # Everything (recommended)
pip install aceteam-aep[safety,proxy]      # Safety detectors + proxy
pip install aceteam-aep                    # Core only (cost tracking + regex safety)

Quick Start — Make OpenClaw (or any agent) Safe

No code changes. Just run the proxy and point your agent at it:

# Terminal 1: Start the AEP safety proxy
aceteam-aep proxy --port 8080

# Terminal 2: Run OpenClaw through the proxy
export OPENAI_BASE_URL=http://localhost:8080/v1
export OPENAI_API_KEY=sk-your-key
openclaw run "analyze these financial statements"

Open http://localhost:8080/aep/ — the dashboard shows every LLM call flowing through in real-time: cost, safety signals, and enforcement decisions.

The proxy intercepts both directions:

• Incoming requests — blocks dangerous prompts before they reach the API
• Outgoing responses — blocks PII, toxic content, and cost anomalies before the agent sees them

Works with OpenClaw, LangChain, CrewAI, curl, or any tool that calls the OpenAI API.

Python SDK — Wrap Your Existing Client

import openai
from aceteam_aep import wrap

client = wrap(openai.OpenAI())

# Use exactly as before — AEP intercepts transparently
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

# AEP tracks everything
print(client.aep.cost_usd)            # $0.000150
print(client.aep.enforcement.action)   # "pass"
print(client.aep.safety_signals)       # []
client.aep.print_summary()             # Colored CLI output

Works with OpenAI, Anthropic, and any OpenAI-compatible client. Sync and async.

import anthropic
from aceteam_aep import wrap

client = wrap(anthropic.Anthropic())
# Same API — client.aep.cost_usd, client.aep.safety_signals, etc.

Safety Signals

Every LLM call is evaluated by pluggable safety detectors:

Detector	What It Catches	Model
PII	SSN, email, phone, credit cards in output	iiiorg/piiranha-v1-detect-personal-information (~110M)
Content Safety	Toxic, harmful, or unsafe content	s-nlp/roberta_toxicity_classifier (~125M)
Cost Anomaly	Spend spikes >5x session average	Statistical (no model)

Models lazy-load on first use, run on CPU. Falls back to regex if transformers not installed.

Enforcement: PASS / FLAG / BLOCK

Every call produces an enforcement decision based on signal severity:

• PASS — No signals or low severity. Safe to proceed.
• FLAG — Medium severity. Route to human review.
• BLOCK — High severity (PII, toxic content). Prevent delivery.

client = wrap(openai.OpenAI())
response = client.chat.completions.create(...)

match client.aep.enforcement.action:
    case "pass":
        return response
    case "flag":
        queue_for_review(response)
    case "block":
        return reject(client.aep.enforcement.reason)

Custom Detectors

from aceteam_aep import wrap
from aceteam_aep.safety.base import SafetySignal

class MyDetector:
    name = "my_detector"

    def check(self, *, input_text, output_text, call_id, **kwargs):
        if "secret" in output_text.lower():
            return [SafetySignal(
                signal_type="data_leak",
                severity="high",
                call_id=call_id,
                detail="Potential secret in output",
            )]
        return []

client = wrap(openai.OpenAI(), detectors=[MyDetector()])

Dashboard

client.aep.serve_dashboard()  # http://localhost:8899

Dark-themed local web UI showing cost, safety status, signal timeline, and call history. Auto-refreshes every 2 seconds.

CLI Output

client.aep.print_summary()

──────────────────────────────────────────────────
  AEP Session Summary
──────────────────────────────────────────────────
  Calls:  5
  Cost:   $0.004200
  Safety: PASS
──────────────────────────────────────────────────

Agent Loop (Advanced)

For building agents from scratch with full AEP compliance:

from aceteam_aep import create_client, run_agent_loop, ChatMessage, tool

client = create_client("gpt-4o", api_key="sk-...")

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"

result = await run_agent_loop(
    client,
    [ChatMessage(role="user", content="Search for AEP protocol")],
    tools=[search],
    system_prompt="You are a helpful assistant.",
)

Workshop Guide

Step-by-step setup in 5 minutes — from install to safety signals firing:

docs/workshop-guide.md

Covers: proxy setup, routing agents (Python/OpenClaw/curl), triggering safety signals, governance headers, custom detectors. Works for workshops, onboarding, or self-guided evaluation.

Providers

• OpenAI (GPT-4o, GPT-5, o1, o3)
• Anthropic (Claude Opus, Sonnet, Haiku)
• Google (Gemini 2.5, 3.0)
• xAI (Grok)
• Ollama (local models)
• OpenAI-compatible (SambaNova, TheAgentic, DeepSeek)