Open Source Workflow Engine: Self-Hosted AI Orchestration

An open-source workflow engine is worth running yourself when your AI jobs wait days, spend money on every retry, or touch data you cannot send to a vendor. For a job that runs in two minutes and fails rarely, it is usually the wrong first move. This post covers where each option breaks, using LangGraph, Temporal, Floneum and Pipelex as the examples.

Checkpoints and durable execution protect different things

Comparisons of LangGraph and Temporal converge on one split. LangGraph checkpoints protect against application-level problems: a bad branch, a pause for human review, a rerun from an earlier state. Temporal protects against infrastructure problems: a container dies mid-step, a host is preempted, a network call hangs. The LangGraph comparison is written by LangChain, the vendor, so read its conclusions with that in mind. The split itself is not controversial.

The consequence is that picking an engine by its feature list misses the question that matters: what happens to the step that was running when the process died. One 2026 comparison puts it this way: LangGraph's flexibility "can hide non-idempotent behavior unless node boundaries are disciplined." If a node sends an email and then the process crashes before the checkpoint is written, resuming re-sends the email. The engine cannot fix that for you. Each side-effecting step needs its own idempotency key regardless of which engine you pick.

Replay rules decide how Temporal feels

Temporal recovers by replaying your workflow code, so that code must be deterministic. The docs say a workflow cannot make API calls or LLM invocations inline and must put them in Activities, and it cannot branch on local time or random values. For an AI workflow this is a clean fit if you accept it: every model call becomes an Activity with its own timeout and retry policy, and the recorded result is reused on replay instead of paying for the call again. It is a poor fit if your team writes agent loops as free-form Python and wants to start in an afternoon. The Cordum comparison adds that Temporal brings operational components you must run on top of those replay constraints.

When a database table is enough

The cheapest engine is a Postgres table and a claim query. Cordum's comparison argues that below roughly 30 seconds of execution, fewer than three external systems, no multi-hour waits and no production side effects, "the operational overhead of a second runtime usually is not justified."

A job table can be small. A scheduler only hands due jobs to a bounded pool of workers. Each run takes a compare-and-set claim on its row before it starts, so two processes during a deploy overlap cannot both fire the same job. Runs get a time limit and a heartbeat so a stuck run can be detected and retried.

Know the ceiling of whatever you adopt. Temporal's Service logs a warning after 10,240 events in a Workflow's Event History and terminates the execution when the history exceeds 51,200 events, and its docs point to Continue-As-New as the way out (Temporal Events and Event History). An agent loop that records an event for every tool call and retry can reach that on a long run, so budget events per run before you choose one workflow per task.

A table is not a general workflow engine. It covers simple cases without a separate cluster. The point at which this stops being enough is when you need durable timers measured in days, signals into a running workflow, or versioned workflow code with in-flight runs. At that point adopting Temporal is cheaper than rebuilding it.

Where the money goes

The expensive failure in an AI workflow is a repeated model call. Whatever engine you choose, make sure model output is persisted at a step boundary before the next step starts. A comparison of Inngest, Temporal, DBOS and LangGraph makes the same point: none of them saves a call you make outside a step boundary. Check this in a test by killing the worker between a model call and the next step, then counting how many times the model was billed.

LangGraph, Floneum and Pipelex in brief

• LangGraph is a Python library for stateful agent graphs with cycles and human-in-the-loop pauses. Use a database checkpointer in production, since the in-memory saver loses its checkpoints on restart.
• Floneum offers a visual graph editor and runs plugins in WebAssembly sandboxes, which matters if you run code you do not fully trust.
• Pipelex is a declarative language for composing AI pipelines as code, which suits teams that want workflows in version control and review.

A migration order that limits risk

Move one low-stakes workflow first. Keep each task a plain function with typed input and output so it can run under a different engine later. Pass references to documents in object storage rather than the documents themselves, since large state payloads slow every checkpoint write. Alert on the dead-letter queue from day one, because a job that fails five times in silence costs more than a job that fails loudly once.

References

• Temporal: Workflow Definition and determinism
• Cordum: Temporal vs LangGraph
• LangChain: LangGraph vs Temporal
• Inngest vs Temporal vs DBOS vs LangGraph
• LangGraph, Floneum, Pipelex
• Temporal: Events and Event History: warning at 10,240 events and termination above 51,200.

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