This comparison has an obvious conflict of interest: the site belongs to Actae. It is written to be checkable anyway, because an inaccurate comparison costs more trust than it earns. Every competitor cell below reflects what these products document as of early October 2026, and the honest answer is that they have all moved toward agents fast. If you are choosing today, verify each cell against current vendor documentation — these products change monthly.

The short version

All of these tools make long-running code survive failures. They differ in what they assume about your code. General workflow engines assume deterministic, replayable workflow functions. AI agents are non-deterministic: the model may say something different on replay, and tool calls have real-world side effects. Actae is built for that case — and, more specifically, for the case where you want to change one decision and keep the work before it.

What is no longer a differentiator

A few years ago, “your agent survives a crash” was enough. It is not anymore. By late 2026:

Temporal ships a generally-available OpenAI Agents SDK integration, plus Vercel AI SDK, Pydantic AI, and Bedrock Strands integrations. Model calls run as Activities; the agent orchestration runs inside a Workflow.
Inngest has a full durable-agent story: each model and tool call is a memoized step, with agents, evals, sandboxes, and variant experiments.
Restate journals agent steps into durable services and virtual objects, with persistent sessions, concurrency control, and rollback, plus integrations for Vercel AI, OpenAI Agents, Pydantic AI, Google ADK, and LangChain.
DBOS runs durable workflows in-process on Postgres, with OpenAI Agents and Pydantic AI integrations, workflow patching, workflow lineage tracking, and workflow fork.
LangGraph checkpointers provide native resume, time travel, and checkpoint forks inside LangGraph.

So “crash recovery” and “don’t re-call the model” are table stakes. The remaining differences are about boundaries and portability.

What you actually need for agents

Resume from the exact step after a crash, without re-calling the model or re-running completed work.
Tool calls that cannot double-execute (send, charge, write) — coordinated, not just wrapped.
Forking from a past step to test a different decision while inheriting the real accumulated state.
Catch-up-then-live delivery so a dashboard, evaluator, or second agent can join mid-run without a hand-built handoff.
Portability across frameworks when one system spans more than one agent stack, now or later.

The first two are now widely available. The last three are where the options diverge, and where Actae is strongest.

Where each option fits

OptionBest whenTrade-off for agents

TemporalYou need mature, general workflow orchestration at scale.Determinism constraints; the agent loop is modeled as a Workflow; Reset/fork is workflow-history level; heavier to operate.

InngestEvent-driven durable steps and agent loops with strong DX, evals, and experiments.Run/step checkpointing and memoization; no fork-with-inherited-state or cross-framework lineage primitive.

RestateDurable RPC and virtual objects; sessions with built-in concurrency control.General-purpose journaling; agent-specific forking, lineage, and structural state diff are yours to build.

DBOSPostgres-backed durable workflows inside your app, with no orchestrator, including workflow fork.Library plus decorators; no framework-neutral substrate, immutable receipts, or structural state diff.

LangGraph checkpointersYou’re all-in on LangGraph and need native resume, time travel, and checkpoint forks.Framework-bound; no fencing across stacks; a checkpoint records state, not external effects.

ActaeAgents with side effects, forking/eval needs, any framework, and a self-hostable boundary.Younger product; no public SLA yet; assisted onboarding.

The first two columns above are deliberately written from each product’s own documentation. Treat them as a starting point for a documentation dive, not a verdict.

When the choice is genuinely another tool

Temporal when a general workflow engine should own the entire application lifecycle and durability should be expressed through the workflow model. Its maturity, scale, and ecosystem are real advantages that Actae does not match.
Inngest when you want event-driven steps, waits, and experiments in one managed developer experience and you are happy with its execution model.
A framework checkpointer when one framework is your whole system and the only requirement is resuming a run.
Postgres plus queues when control is the point and your team is prepared to own ordering, leases, idempotency, and recovery semantics deliberately.

When not to choose Actae: you need a proven enterprise SLA today, or your workflows are not agent-shaped. Use Temporal or DBOS with Postgres. Actae is honest that it is earlier — and that a self-hosted deployment is how its early customers put the operational controls in their own hands.

What the fork is anchored to

Several of these tools can now fork. The difference is the anchor. Temporal’s Reset replays workflow history to a prior point; DBOS forks a workflow at a step; LangGraph forks a checkpoint inside a LangGraph thread. Actae forks at an exact logical agent step, copies the state snapshot at or before that step into a new channel, and returns an immutable fork receipt describing the requested and resolved boundary, the inherited state version, and a SHA-256 fingerprint of the state — with a reproducibility grade. Steps before the boundary never re-run.

That anchoring is what makes the follow-on questions answerable: structural state diff between two forks (“what did one know that the other didn’t?”), a decision trail connecting lineage to tool results, and the same primitive working from Python, Go, or TypeScript, under LangGraph, LangChain, the Claude Agent SDK, the OpenAI Agents SDK, CrewAI, Copilot, or a plain async for loop.

The other half of the wedge is the idempotent tool-execution ledger. Actae never executes your tool; it coordinates the retry. A claim is keyed by a stable operation key and a parameter fingerprint, guarded by leases and fencing tokens. A retry of a completed operation replays the stored result instead of firing the external call again, and a stale worker is fenced out. That is a coordination guarantee, not a serialization trick — and it is what lets a fork avoid re-firing a side effect the parent already performed.

The last shared primitive is replay-to-live: a subscriber catches up from a cursor, crosses a deterministic synchronization point, and continues live over one WebSocket. A dashboard, evaluator, or second agent joins without an application-built handoff. See the 75-second walkthrough for the crash → resume → fork → compare flow against representative data.

What Actae does not claim

It is not a workflow engine or a scheduler. It does not own the execution loop and does not schedule work.
It is not exactly-once by itself. A durable ledger closes the duplicate window only when the downstream honors the stable idempotency key. Without that, reconciliation remains the caller’s job — the ambiguous window becomes queryable instead of invisible.
It does not sell a public SLA yet. The operating posture is stated plainly rather than overclaimed, as a benchmark of what a crash actually costs makes concrete.

Try it against your own agent

You can also judge it locally: download the free self-hosted build, run ./actae, and fork a run on your own machine with no account or card. Or bring one workflow that already runs for ten minutes or longer and Actae will review the architecture, provision a dedicated Cloud endpoint, and help you prove crash recovery and forking — measured, not asserted. The deterministic demo records 8,260 tokens saved (46.8%) by forking past the first five steps of a ten-step pipeline, and the comparison page lists the choices plainly.