onceonly-sdk 3.0.2

Python SDK for OnceOnly idempotency API

OnceOnly Python SDK

AI Agent Execution & Governance Layer

Exactly-once execution + runtime safety + agent control plane.

Website • Docs • API Reference • Examples

---

🎯 What is OnceOnly?

The problem: AI agents are non-deterministic. They retry failed calls, re-run tools, crash mid-execution, and replay events. This causes duplicate payments, repeated emails, and inconsistent state.

The solution: OnceOnly sits between your AI and the real world, guaranteeing:

✅ Exactly-once execution - Same input = same result, always
✅ Crash safety - Worker dies? Pick up where you left off
✅ Retry safety - Agent retries? We deduplicate automatically
✅ Budget control - Cap spending per agent/hour/day
✅ Permission enforcement - Whitelist/blacklist tools
✅ Kill switch - Disable rogue agents instantly
✅ Forensic audit - Complete action history

This isn't just idempotency. This is an AI Agent Control Plane.

---

⚡ Quick Start (30 seconds)

pip install onceonly-sdk

from onceonly import OnceOnly

client = OnceOnly(api_key="once_live_...")

# Prevent duplicate webhook processing
result = client.check_lock(key="webhook:stripe:evt_123", ttl=3600)

if result.duplicate:
    return {"status": "already_processed"}

# Process webhook (runs exactly once)
process_payment(webhook_data)

That's it. You just made your webhook idempotent.

---

🚀 5-Minute Tutorial

1️⃣ Basic Deduplication (Webhooks, Cron Jobs, Workers)

from onceonly import OnceOnly

client = OnceOnly(api_key="once_live_...")

# Stripe webhook
@app.post("/webhooks/stripe")
def stripe_webhook(event_id: str):
    result = client.check_lock(
        key=f"stripe:{event_id}",
        ttl=7200  # 2 hours
    )
    
    if result.duplicate:
        return {"status": "ok"}  # Already processed
    
    # Process event (guaranteed exactly-once)
    handle_payment_succeeded(event_id)
    return {"status": "processed"}

2️⃣ AI Agent with Budget & Permissions

from onceonly import OnceOnly

client = OnceOnly(api_key="once_live_...")

# Set policy (one-time setup)
client.gov.upsert_policy({
    "agent_id": "billing-agent",
    "max_actions_per_hour": 200,
    "max_spend_usd_per_day": 50.0,
    "allowed_tools": ["stripe.charge", "send_email"],
    "blocked_tools": ["delete_user"]
})

# Execute tool with enforcement
result = client.ai.run_tool(
    agent_id="billing-agent",
    tool="stripe.charge",
    args={"amount": 9999, "currency": "usd"},
    spend_usd=0.5,  # Track API cost
)

if result.allowed:
    print(f"Charged: {result.result}")
elif result.decision == "blocked":
    print(f"Agent blocked: {result.policy_reason}")

3️⃣ Exactly-Once Function Execution

from onceonly import OnceOnly, idempotent_ai

client = OnceOnly(api_key="once_live_...")

@idempotent_ai(
    client,
    key_fn=lambda user_id: f"welcome:email:{user_id}",
    ttl=86400  # 24 hours
)
def send_welcome_email(user_id: str):
    # This runs exactly ONCE per user_id
    # Even if called 1000 times concurrently
    email_service.send(
        to=get_user_email(user_id),
        template="welcome"
    )
    return {"sent": True}

# All these calls get the same result
send_welcome_email("user_123")  # Sends email
send_welcome_email("user_123")  # Returns cached result
send_welcome_email("user_123")  # Returns cached result

---

✅ Cheat-Sheet (Pick The Right Call)

I want…

• Idempotent webhook/cron/job: check_lock(key, ttl, meta)
• Long-running server job: ai.run_and_wait(key, ttl, metadata)
• Governed tool call (agent + tool): ai.run_tool(agent_id, tool, args, spend_usd)
• Local side-effect exactly once: ai.run_fn(key, fn, ttl)
• Decorator version: @idempotent or @idempotent_ai

Async equivalents

• check_lock_async
• ai.run_and_wait_async
• ai.run_tool_async
• ai.run_fn_async

---

🤖 Full LLM Agent Flow (No OnceOnly vs OnceOnly)

These two examples show why OnceOnly matters in production.

Without OnceOnly (duplicates + money loss)

# examples/ai/agent_full_flow_no_onceonly.py
decision = llm_decide()
payload = {"tool": decision["tool"], "args": decision["args"]}

# A retry or crash can re-run this call
call_tool(payload)
call_tool(payload)  # duplicate charge

With OnceOnly (deduped + governed)

# examples/ai/agent_full_flow_onceonly.py
res = client.ai.run_tool(
    agent_id="billing-agent",
    tool="stripe.charge",
    args={"amount": 9999, "currency": "usd", "user_id": "u_42"},
    spend_usd=0.5
)

if res.allowed:
    print(res.result)
else:
    print("Blocked:", res.policy_reason)

Why this matters

• Prevents duplicate charges on retries
• Enforces budgets and permissions
• Gives audit trails for every tool call

Cost impact (simple example)

• Without OnceOnly: 1 retry on a $99 charge = $198
• With OnceOnly: 1 retry on a $99 charge = $99

Flow diagram (simplified)

LLM -> Tool Call -> External System
  |       |             |
  |       |__ retry ____|   (duplicate charge)
  |
OnceOnly in between
  |
LLM -> OnceOnly -> Tool Call -> External System
          |
          |__ duplicate detected -> blocked

---

📚 Complete Feature Matrix

Feature	Description	Use Case
check_lock()	Fast idempotency primitive	Webhooks, cron jobs, workers
ai.run_and_wait()	Long-running AI jobs	Image gen, video processing, reports
ai.run_tool()	Governance tool runner	Tool calls with budgets/permissions
ai.run_fn()	Local exactly-once execution	Payments, emails, database writes
@idempotent_ai	Decorator for functions	Simple exactly-once guarantee
gov.upsert_policy()	Set agent limits	Budget caps, tool permissions
gov.disable_agent()	Kill switch	Emergency stop
gov.agent_logs()	Audit trail	Forensics, compliance
gov.agent_metrics()	Usage stats	Monitoring, alerting

---

🧠 Architecture Layers

OnceOnly provides 5 layers of safety:

┌─────────────────────────────────────────────────────────┐
│ L5: Agent Governance (policies, kill switch, audit)    │
├─────────────────────────────────────────────────────────┤
│ L4: Decorator Runtime (@idempotent_ai)                 │
├─────────────────────────────────────────────────────────┤
│ L3: Local Side-Effects (ai.run_fn)                     │
├─────────────────────────────────────────────────────────┤
│ L2: AI Job Orchestration (ai.run_and_wait)             │
├─────────────────────────────────────────────────────────┤
│ L1: Idempotency Primitive (check_lock)                 │
└─────────────────────────────────────────────────────────┘

Pick the layer that fits your use case. They compose cleanly.

---

💎 Golden Example (Payment with Full Safety)

This example shows complete runtime + governance safety:

from onceonly import OnceOnly, idempotent_ai
import stripe

client = OnceOnly(api_key="once_live_...")

# 1. Set governance policy (one-time)
client.gov.upsert_policy({
    "agent_id": "billing-agent",
    "max_actions_per_hour": 100,
    "max_spend_usd_per_day": 25.0,
    "allowed_tools": ["stripe.charge", "send_receipt"],
    "blocked_tools": ["delete_user", "refund_all"]
})

# 2. Define exactly-once payment function
@idempotent_ai(
    client,
    key_fn=lambda user_id, amount: f"charge:{user_id}:{amount}",
    ttl=300,  # 5 minutes
    metadata_fn=lambda u, a: {
        "user_id": u,
        "amount_cents": a,
        "agent": "billing-agent"
    }
)
def charge_user(user_id: str, amount_cents: int):
    """Charge user - guaranteed exactly once"""
    return stripe.Charge.create(
        amount=amount_cents,
        currency="usd",
        customer=get_stripe_customer_id(user_id)
    )

# 3. Execute with full safety
result = charge_user("user_42", 9999)

if result.status == "completed":
    charge_id = result.result["data"]["id"]
    print(f"✅ Charged: {charge_id}")
else:
    print(f"❌ Failed: {result.error_code}")

Guarantees:

• ✅ Charged exactly once (even if retried 1000x)
• ✅ Budget enforced (won't exceed $25/day)
• ✅ Tool allowed (stripe.charge in whitelist)
• ✅ Crash safe (worker dies? resumes automatically)
• ✅ Audit logged (forensic trail for compliance)

---

🛡️ Governance & Safety

Agent Policies

Control what agents can do:

# Strict policy (whitelist only)
client.gov.upsert_policy({
    "agent_id": "readonly-agent",
    "max_actions_per_hour": 500,
    "allowed_tools": ["get_user", "search", "list_items"],
    "blocked_tools": []  # Everything else blocked
})

# Moderate policy (blacklist dangerous tools)
client.gov.upsert_policy({
    "agent_id": "support-agent",
    "max_actions_per_hour": 200,
    "max_spend_usd_per_day": 50.0,
    "blocked_tools": ["delete_user", "stripe.charge"]
})

# Per-tool limits
client.gov.upsert_policy({
    "agent_id": "billing-agent",
    "max_calls_per_tool": {
        "stripe.refund": 5,    # Max 5 refunds/day
        "send_email": 100      # Max 100 emails/day
    }
})

Policy Templates

Use pre-configured templates:

# Quick setup with sensible defaults
policy = client.gov.policy_from_template(
    agent_id="new-agent",
    template="moderate",  # strict|moderate|permissive|read_only|support_bot
    overrides={
        "max_actions_per_hour": 300,
        "blocked_tools": ["delete_user"]
    }
)

Available templates (server defaults):

• strict
• moderate
• permissive
• read_only
• support_bot

Kill Switch

Instantly disable rogue agents:

# Emergency stop
client.gov.disable_agent(
    "rogue-agent",
    reason="Suspicious behavior detected"
)

# Re-enable after investigation
client.gov.enable_agent("rogue-agent")

Audit & Forensics

Complete action history:

# Get recent actions
logs = client.gov.agent_logs("billing-agent", limit=100)

for log in logs:
    print(f"{log.ts}: {log.tool} - {log.decision}")
    print(f"  Reason: {log.policy_reason or log.reason}")
    print(f"  Risk: {log.risk_level}")
    print(f"  Cost: ${log.spend_usd}")

# Get metrics
metrics = client.gov.agent_metrics("billing-agent", period="day")
print(f"Actions: {metrics.total_actions}")
print(f"Blocked: {metrics.blocked_actions}")
print(f"Spend: ${metrics.total_spend_usd}")
print(f"Top tools: {metrics.top_tools}")

---

🔌 Framework Integrations

LangChain

from langchain_core.tools import tool
from onceonly import OnceOnly
from onceonly.integrations.langchain import make_idempotent_tool

client = OnceOnly(api_key="once_live_...")

@tool
def send_email(to: str, subject: str, body: str) -> str:
    """Send email to user"""
    email_service.send(to=to, subject=subject, body=body)
    return f"Email sent to {to}"

# Wrap with idempotency
idempotent_send_email = make_idempotent_tool(
    send_email,
    client=client,
    key_prefix="agent:email",
    ttl=3600
)

# Use in agent
from langchain.agents import AgentExecutor, create_react_agent

agent = create_react_agent(llm, tools=[idempotent_send_email], prompt)
executor = AgentExecutor(agent=agent, tools=[idempotent_send_email])

# Agent can retry - we guarantee exactly-once execution
result = executor.invoke({"input": "Send welcome email to [e-mail]"})

FastAPI

from fastapi import FastAPI, Depends, HTTPException
from onceonly import OnceOnly
import os

app = FastAPI()

def get_onceonly() -> OnceOnly:
    return OnceOnly(api_key=os.environ["ONCEONLY_API_KEY"])

@app.post("/webhooks/stripe")
async def stripe_webhook(
    event: dict,
    client: OnceOnly = Depends(get_onceonly)
):
    result = await client.check_lock_async(
        key=f"stripe:{event['id']}",
        ttl=7200,
        meta={"type": event["type"]}
    )
    
    if result.duplicate:
        return {"status": "duplicate"}
    
    await process_stripe_event(event)
    return {"status": "processed"}

---

🧰 Tools Registry (User-Owned Tools)

Register your own tools (URLs) and enforce permissions per agent.

# Register a tool (requires Pro or Agency)
tool = client.gov.create_tool({
    "name": "send_email",
    "url": "https://example.com/tools/send_email",
    "scope_id": "global",
    "auth": {"type": "hmac_sha256", "secret": "your_shared_secret"},
    "timeout_ms": 15000,
    "max_retries": 2,
    "enabled": True,
    "description": "Send email to user"
})

# Toggle a tool
client.gov.toggle_tool("send_email", enabled=False)

# List tools
tools = client.gov.list_tools(scope_id="global")

Tools registry limits by plan

• Pro: 10 tools
• Agency: 500 tools

Note: Tools registry is not available on Free/Starter.

Rules & expectations (important)

• name must be unique per scope_id and match ^[a-zA-Z0-9_.:-]+$
• scope_id lets you namespace tools (e.g. global or agent:billing-agent)
• auth.type currently supports hmac_sha256 (use a shared secret)
• Your tool endpoint should verify HMAC and be idempotent on its side

---

📖 API Reference

Core Client

from onceonly import OnceOnly

client = OnceOnly(
    api_key="once_live_...",
    base_url="https://api.onceonly.tech/v1",  # optional
    timeout=5.0,                               # HTTP timeout
    fail_open=True,                            # graceful degradation
    max_retries_429=3,                         # auto-retry on rate limit
    retry_backoff=0.5,                         # initial backoff (seconds)
    retry_max_backoff=10.0                     # max backoff (seconds)
)

API Endpoints Map (Public)

Use this map to find the correct endpoint category quickly:

• Core: GET /v1/me, GET /v1/usage, GET /v1/usage/all, GET /v1/events, GET /v1/metrics
• Idempotency: POST /v1/check-lock
• AI Jobs: POST /v1/ai/run, GET /v1/ai/status, GET /v1/ai/result
• AI Lease (local side-effects): POST /v1/ai/lease, POST /v1/ai/extend, POST /v1/ai/complete, POST /v1/ai/fail, POST /v1/ai/cancel
• Governance (policies): POST /v1/policies/{agent_id}, POST /v1/policies/{agent_id}/from-template, GET /v1/policies, GET /v1/policies/{agent_id}
• Governance (agents): POST /v1/agents/{agent_id}/disable, POST /v1/agents/{agent_id}/enable, GET /v1/agents/{agent_id}/logs, GET /v1/agents/{agent_id}/metrics
• Tools Registry: POST /v1/tools, GET /v1/tools, GET /v1/tools/{tool}, POST /v1/tools/{tool}/toggle, DELETE /v1/tools/{tool}

Idempotency

# Sync
result = client.check_lock(
    key="order:12345",
    ttl=3600,           # Lock duration (seconds)
    meta={"user_id": 42}  # Optional metadata
)

# Async
result = await client.check_lock_async(key="order:12345", ttl=3600)

# Check result
if result.duplicate:
    print(f"Duplicate! First seen: {result.first_seen_at}")
else:
    print("First time - proceed with action")

AI Execution

# Long-running job (server-side)
result = client.ai.run_and_wait(
    key="report:monthly:2024-01",
    ttl=1800,                      # Job timeout (seconds)
    timeout=120.0,                 # Polling timeout
    poll_min=1.0,                  # Min poll interval
    poll_max=10.0,                 # Max poll interval
    metadata={"month": "2024-01"}
)

# Governance tool runner (agent + tool)
tool_res = client.ai.run_tool(
    agent_id="billing-agent",
    tool="stripe.charge",
    args={"amount": 9999, "currency": "usd"},
    spend_usd=0.5
)
if tool_res.allowed:
    print(tool_res.result)
else:
    print(f"Blocked: {tool_res.policy_reason}")

# Async tool runner
tool_res = await client.ai.run_tool_async(
    agent_id="billing-agent",
    tool="stripe.charge",
    args={"amount": 9999, "currency": "usd"},
    spend_usd=0.5
)
if tool_res.allowed:
    print(tool_res.result)
else:
    print(f"Blocked: {tool_res.policy_reason}")

# Local function execution
result = client.ai.run_fn(
    key="email:welcome:user123",
    fn=lambda: send_email(...),
    ttl=300,
    wait_on_conflict=True,  # Wait if another process executing
    timeout=60.0,
    error_code="email_failed"
)

# Check status only (no polling)
status = client.ai.status("report:monthly:2024-01")
print(f"Status: {status.status}, TTL: {status.ttl_left}s")

# Get result
result = client.ai.result("report:monthly:2024-01")
if result.status == "completed":
    print(result.result)

### AI Modes (Choose One)

| Mode | Use When | Call | Result Type |
|------|----------|------|-------------|
| **Job (server-side)** | Long-running tasks | `ai.run_and_wait(key=...)` | `AiResult` |
| **Tool (governed)** | Agent tool execution | `ai.run_tool(agent_id=..., tool=...)` | `AiToolResult` |
| **Local side-effects** | Your code does the work | `ai.run_fn(key=..., fn=...)` | `AiResult` |

### AI Result Shapes (AI-friendly)

```python
# Tool result (governance)
AiToolResult = {
    "ok": bool,
    "allowed": bool,
    "decision": str,         # "executed" | "blocked" | "dedup"
    "policy_reason": str | None,
    "risk_level": str | None,
    "result": dict | None,
}

# Job result (run_and_wait / result)
AiResult = {
    "ok": bool,
    "status": str,           # "completed" | "failed" | "in_progress"
    "key": str,
    "result": dict | None,
    "error_code": str | None,
    "done_at": str | None,
}

Tool: Happy vs Blocked

res = client.ai.run_tool(
    agent_id="billing-agent",
    tool="stripe.refund",
    args={"charge_id": "ch_123", "amount": 500},
    spend_usd=0.2
)

if res.allowed:
    print("OK", res.result)
else:
    print("BLOCKED", res.policy_reason)

### Decorators

```python
from onceonly import idempotent, idempotent_ai

# Basic idempotency
@idempotent(client, key_prefix="payment", ttl=3600)
def process_payment(order_id: str):
    # Runs once per order_id
    stripe.charge(...)

# AI lease execution
@idempotent_ai(
    client,
    key_fn=lambda user_id: f"onboard:{user_id}",
    ttl=600,
    metadata_fn=lambda uid: {"user": uid}
)
def onboard_user(user_id: str):
    # Exactly-once, even across multiple workers
    create_account(user_id)
    send_welcome_email(user_id)
    return {"onboarded": True}

Governance

# Set policy
policy = client.gov.upsert_policy({
    "agent_id": "my-agent",
    "max_actions_per_hour": 200,
    "max_spend_usd_per_day": 50.0,
    "allowed_tools": ["tool_a", "tool_b"],
    "blocked_tools": ["dangerous_tool"],
    "max_calls_per_tool": {"tool_a": 10}
})

# From template
policy = client.gov.policy_from_template(
    agent_id="my-agent",
    template="moderate",
    overrides={"max_actions_per_hour": 300}
)

# Kill switch
status = client.gov.disable_agent("my-agent", reason="Testing")
status = client.gov.enable_agent("my-agent")

# Audit
logs = client.gov.agent_logs("my-agent", limit=100)
metrics = client.gov.agent_metrics("my-agent", period="day")

---

⚙️ Configuration

Environment Variables

export ONCEONLY_API_KEY="once_live_..."
export ONCEONLY_BASE_URL="https://api.onceonly.tech/v1"  # optional

Fail-Open Behavior

Network/server failures don't break your app (graceful degradation):

client = OnceOnly(
    api_key="...",
    fail_open=True  # default: allows execution on timeout/5xx
)

Fail-open NEVER applies to:

• 401/403 (auth errors) → Always blocks
• 402 (usage limit) → Always blocks
• 422 (validation) → Always blocks
• 429 (rate limit) → Retries with backoff

Connection Pooling

import httpx
from onceonly import OnceOnly

# Reuse HTTP connections
sync_client = httpx.Client(
    timeout=10.0,
    limits=httpx.Limits(max_keepalive_connections=20)
)

client = OnceOnly(
    api_key="...",
    sync_client=sync_client
)

# Close when done
client.close()

Context Managers

# Auto-cleanup
with OnceOnly(api_key="...") as client:
    result = client.check_lock(key="task", ttl=300)

# Async
async with OnceOnly(api_key="...") as client:
    result = await client.check_lock_async(key="task", ttl=300)

---

🚨 Common Patterns & Best Practices

✅ DO

# ✅ Use specific, deterministic keys
key = f"payment:{order_id}:{user_id}"

# ✅ Set appropriate TTLs
ttl = 3600  # 1 hour for webhooks
ttl = 86400  # 24 hours for daily jobs

# ✅ Add metadata for debugging
meta = {"user_id": 123, "amount": 9999, "source": "web"}

# ✅ Handle duplicates gracefully
if result.duplicate:
    logger.info(f"Duplicate detected: {result.key}")
    return cached_response

# ✅ Use decorators for simplicity
@idempotent_ai(client, key_fn=lambda x: f"task:{x}")
def my_task(x): ...

❌ DON'T

# ❌ Don't use random/timestamp in keys
key = f"payment:{uuid.uuid4()}"  # Every call is "unique"
key = f"task:{time.time()}"      # Never deduplicates

# ❌ Don't set TTL too short
ttl = 1  # Retries will leak through

# ❌ Don't ignore duplicate status
result = client.check_lock(...)
process_payment()  # Always runs!

# ❌ Don't catch and swallow errors silently
try:
    client.check_lock(...)
except: pass  # Lose safety guarantees

---

🐛 Troubleshooting

"Unauthorized" (401/403)

Cause: Invalid API key

# ❌ Wrong
client = OnceOnly(api_key="sk_test_...")

# ✅ Correct
client = OnceOnly(api_key="once_live_...")

"Usage limit reached" (402)

Cause: Exceeded monthly quota for your plan

Solution: Upgrade at https://onceonly.tech/pricing

# Check current usage
usage = client.usage(kind="make")
print(f"Used: {usage['usage']} / {usage['limit']}")

"Rate limit exceeded" (429)

Cause: Too many requests per second

Solution: Enable auto-retry:

client = OnceOnly(
    api_key="...",
    max_retries_429=3,      # Auto-retry up to 3 times
    retry_backoff=0.5,       # Start with 0.5s delay
    retry_max_backoff=10.0   # Cap at 10s
)

Duplicates not being detected

Cause: Key is not deterministic

# ❌ Wrong: random UUID
key = f"order:{uuid.uuid4()}"

# ✅ Correct: stable identifier
key = f"order:{order_id}"

Agent blocked by policy

Cause: Policy restrictions

# Check what happened
logs = client.gov.agent_logs("my-agent", limit=10)
for log in logs:
    if log.decision == "blocked":
        print(f"Blocked: {log.tool} - {log.policy_reason or log.reason}")

# Adjust policy
client.gov.upsert_policy({
    "agent_id": "my-agent",
    "allowed_tools": ["tool_a", "tool_b", "tool_c"],  # Add tool_c
})

📊 Feature Availability

Feature	Free	Starter	Pro	Agency
Core Idempotency
check_lock()	1K/mo	20K/mo	200K/mo	2M/mo
ai.run_and_wait()	3K/mo	100K/mo	1M/mo	10M/mo
Agent Governance
gov.upsert_policy()	❌	❌	✅ Limited	✅ Full
gov.agent_logs()	❌	❌	✅	✅
gov.agent_metrics()	❌	❌	✅	✅
gov.disable_agent() (Kill switch)	❌	❌	❌	✅
gov.enable_agent()	❌	❌	❌	✅
Policy Features
Budget limits (max_spend_usd_per_day)	❌	❌	✅	✅
Tool blocklist (blocked_tools)	❌	❌	✅	✅
Tool whitelist (allowed_tools)	❌	❌	❌	✅
Per-tool limits (max_calls_per_tool)	❌	❌	✅	✅

Pro Plan: Limited governance (no allowed_tools whitelist, no kill switch)
Agency Plan: Full governance (whitelist, kill switch, anomaly detection)

📈 Plan Limits (Defaults)

These are the default limits enforced by the API (may be configured by the server):

Plan	check_lock (make)	ai (runs)	Default TTL	Max TTL	Tools Registry Limit
Free	1K / month	3K / month	60s	1h	Not available
Starter	20K / month	100K / month	1h	24h	Not available
Pro	200K / month	1M / month	6h	7d	10 tools
Agency	2M / month	10M / month	24h	30d	500 tools

Pro vs Agency differences (important):

• Pro: Governance is limited (no allowed_tools whitelist, no kill switch).
• Agency: Full governance, including tool whitelist + kill switch.

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📊 Production Checklist

Before going live:

• Use production API key (once_live_...)
• Set appropriate TTLs (not too short, not too long)
• Enable auto-retry (max_retries_429=3)
• Add metadata for debugging (meta={"user": ...})
• Monitor usage (check client.usage() regularly)
• Set up governance for AI agents
• Test fail-open behavior (simulate API downtime)
• Review audit logs periodically
• Set up alerts for blocked actions

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🔗 Links

• Website: https://onceonly.tech
• Documentation: https://docs.onceonly.tech
• API Reference: https://docs.onceonly.tech/reference/idempotency/
• Python SDK Docs: https://docs.onceonly.tech/sdk/python
• GitHub: https://github.com/onceonly-tech/onceonly-python
• PyPI: https://pypi.org/project/onceonly-sdk/
• Support: [e-mail]

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📄 License

MIT License - see LICENSE file for details.

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

✅ Tests

pytest -q

Integration smoke tests (live API):

export TEST_API_KEY="once_live_..."
export TEST_BASE_URL="https://api.onceonly.tech"
pytest -q -m integration

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⭐ Support

If OnceOnly helps your project, give us a star on GitHub!

Questions? Open an issue or email [e-mail]

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_{Built with ❤️ by the OnceOnly team}