How to Build a Temporal Knowledge Graph with Graphiti
Graphiti builds knowledge graphs that track how facts change over time, so your AI agent knows what is still true. Here is how to install…
Temporal is a durable execution engine. It runs code as a workflow that survives process crashes, restarts and network outages — state is persisted, so a multi-day AI agent job resumes exactly where it stopped. The core is open source (MIT); Temporal Cloud is the managed version.
Temporal grew out of Uber’s Cadence project and launched as its own company in 2019. You write workflows as ordinary functions in Go, Java, TypeScript, Python or .NET, and the Temporal server records every step in an event history. If the worker dies, another picks up the workflow from the last recorded step. That is the whole pitch: no hand-rolled retry queues or database state machines.
Agent tasks are long and flaky: they call LLM APIs, run tools, wait on humans and can take hours. Temporal gives them durable retries, timeouts and signals, so an agent that fails at step 7 does not restart at step 1 and burn tokens again.
Temporal is heavy for a simple cron job or a short request-response API — the operational overhead is not worth it. The programming model has real rules (determinism, no direct I/O inside workflows) that trip up newcomers, and self-hosting a production cluster needs Cassandra or PostgreSQL plus monitoring. Expect a learning curve of a few days.