Provenance models your system's behavior as a causal graph. Every interaction linked to what caused it and what it triggered — across services, queues, and time.
The problem
Microservices, queues, webhooks, Lambdas — your operations span dozens of systems. When something breaks, you jump between five tools trying to piece together what happened.
Logs show what — never why
No link between related events across services
Alerts fire after users already noticed
Debugging means 5 tools, 1 hour, 3 engineers
AI can't reason about unstructured noise
Business teams can't self-serve answers
The insight
Every action in your system causes other actions. A payment triggers inventory, which triggers shipping, which triggers a notification. That's not a log line — it's a graph of cause and effect.
The atomic unit. Every time something happens — a user acts, a service calls another, a webhook fires — that's an interaction. Structured, typed, and linked to what caused it.
The full business process. Order-to-cash. Signup-to-activation. Payment-to-delivery. One ID links every interaction in the chain — across services, queues, and time.
One interaction → full causal chain
5 services. 1 Unit of Work. Full causal reconstruction.
A new generation
Logs gave us data. Dashboards gave us visibility. Provenance gives us understanding.
Raw text output. Manual grep. No structure. No correlation. Debugging by reading thousands of lines.
Unified dashboards. Metrics, traces, logs in one place. Better — but still fragmented at the model level. You see symptoms, not causes.
Native causal graph. Business-process-aware. Every interaction linked to its cause and effect. Full system behavior reconstruction from a single ID.
How it works
An order flows through payment, fraud, inventory, shipping, and notifications. Provenance captures the full causal chain as a single Unit of Work.
UOW: 7f3a9c2dCapabilities
Every interaction links to its cause and its effects. Navigate the full chain — not isolated events.
One ID connects an entire business process across services, queues, and time. From trigger to consequence.
When something happens, fire Slack messages, emails, webhooks, or Lambda functions. No polling. No cron.
Capture exceptions with stack traces, grouped by fingerprint. Each error links to the full workflow that caused it.
Structured causal history that AI agents can query, reason about, and act on. Built for RAG and MCP.
Use alongside your existing tools. Ingest spans from any OTel-compatible system into the causal graph.
Reactive by default
Provenance doesn't just record what happened. It reacts. Define subscriptions — when a condition is met, the system responds automatically.
Slack
Post to channels on any event
SendGrid templates with dynamic data
Webhooks
Hit any URL with the full payload
Lambda
Run custom logic on every trigger
Reactions are part of the causal graph — they appear as child interactions in the Unit of Work tree.
AI-native
Logs are noise to LLMs. Provenance gives AI agents structured, causal, queryable system history — ready for reasoning.
AI agents query your system history, investigate incidents, and create configurations using natural language.
Every interaction is typed, timestamped, and causally linked — ideal for retrieval-augmented generation.
Trace any outcome back through the chain of events that caused it. Full causality for AI decision-making.
Your system's complete behavioral history in a format AI can understand, search, and act on.
Integrate in minutes
Node.js
npm i @stdiolabs/provenance-sdkPython
pip install provenance-sdkJava
provenance-sdk-javaTypeScript
npm i @stdiolabs/provenance-sdk-ts$ provenance track \
-r order-123 \
-t ORDER \
-a CREATED
✔ Interaction recorded (12ms)
$ provenance trace --uow 7f3a9c2d
ORDER/CREATED web-app
PAYMENT/CAPTURED stripe
INVENTORY/RSRVD warehouse
SHIPMENT/SENT logisticsBeta Access
Full access to every feature. No credit card. No catch.
Start building your causal graph today. Free tier included.
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