See what your AI decided, why, and what happened next
Control-plane observability for agentic workflows. Trace decisions, surface risk signals, enforce policy checks, and maintain audit-ready logs — before your AI takes action.
Private beta — open to teams building agentic AI systems
Private Beta
Instrument Your AI in Minutes
Wrap your existing OpenAI, Anthropic, or any LLM calls. No proxies, no lock-in.
from ailogbook import AILogsClient
import openai
client = AILogsClient(
base_url="https://api.ailogs.io",
api_key="YOUR_KEY_ID:YOUR_SECRET"
)
with client.trace(trace_key="support-agent-run") as t:
# Record each step of your agent
t.step(step_type="model_call", name="analyze_ticket",
model_name="gpt-4o")
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": ticket}]
)
# Record the decision with risk scoring
t.decision(
decision_type="action_selection",
decision_label="ticket_resolution",
decision_value={"action": "refund", "amount": 250},
confidence=0.87,
action_status="requires_review",
risk_flags=["financial_action"],
)
How It Works
Install & Instrument
pip install ailogbook. Wrap your AI calls in client.trace(). Record decisions and policy checks with a few lines of code.
Observe in Real Time
Every trace, decision, and risk signal appears in your dashboard instantly. Filter by status, risk level, or time range.
Review & Export
Approve or reject flagged decisions. Export audit trails for compliance. Get webhook alerts for policy violations.
Key Features
Everything you need to observe, govern, and audit your AI systems.
Security & Tenant Isolation
AI Logbook isolates tenant data by default and encrypts everything in transit and at rest.
Private Beta — Now Open
We're building AI Logbook with early design partners who need decision audit trails and risk observability for agentic workflows. Request an invite to shape the product.
Request Access