taquba-workflow

Durable execution on object storage: an at-least-once workflow runtime on top of the Taquba durable task queue.

Part of the Taquba ecosystem; see the workspace README for the queue core and the other crates that compose with this one.

taquba-workflow provides the durable machinery for any multi-step process that benefits from durable state between steps: idempotent step execution, retries with backoff, graceful restart, and terminal-state notifications. Implement StepRunner with bytes-in / bytes-out per-step logic; the runtime persists everything else.

Particularly well-suited for AI agent runs (see examples/rig_agent.rs for a Rig integration), but the runtime itself is framework-neutral and equally usable for ETL pipelines, document processing, payment flows, etc.

What this is / isn't

taquba-workflow is an imperative step orchestrator: at each step the runner decides what happens next via StepOutcome (Continue, Succeed, Fail, Cancel). External cancellation is supported via WorkflowRuntime::cancel. It is not:

• A DAG executor. There's no declarative graph, no fan-out / fan-in, no dependency-driven scheduling.
• An event-sourced workflow engine. There's no event-history replay, no per-side-effect recording.

Within the ecosystem, taquba-jobs is the sibling crate for single-shot typed tasks: use it when the caller awaits a typed return value and there are no intermediate steps to persist; use a workflow (even a single-step one) when the caller observes the run through cancellation and a terminal hook rather than awaiting a returned value. taquba-bulk builds on this crate to run one pipeline over many inputs with batch progress and cost rollup.

Install

cargo add taquba-workflow taquba
cargo add tokio --features full

Enable the webhooks feature for WebhookTerminalHook:

cargo add taquba-workflow --features webhooks

Configuring the queue

Per-queue retention (QueueConfig::keep_done_jobs and QueueConfig::dead_retention) is set on the taquba::Queue before it's handed to the runtime. Choose an explicit name via WorkflowRuntimeBuilder::queue_name and key OpenOptions::queue_configs on the same string.

use std::collections::HashMap;
use std::sync::Arc;
use std::time::Duration;
use taquba::{OpenOptions, Queue, QueueConfig, object_store::memory::InMemory};
use taquba_workflow::{NoopTerminalHook, StepError, StepOutcome, StepRunner, WorkflowRuntime, Step};

struct EchoRunner;
impl StepRunner for EchoRunner {
    async fn run_step(&self, step: &Step) -> Result<StepOutcome, StepError> {
        Ok(StepOutcome::Succeed { result: step.payload.clone() })
    }
}

let store = Arc::new(InMemory::new());
let opts = OpenOptions {
    queue_configs: HashMap::from([(
        "agent-runs".to_string(),
        QueueConfig {
            keep_done_jobs: Some(Duration::from_secs(24 * 60 * 60)),
            ..QueueConfig::default()
        },
    )]),
    ..OpenOptions::default()
};
let queue = Arc::new(Queue::open_with_options(store.clone(), "db", opts).await?);
let runtime = WorkflowRuntime::builder(queue, store, EchoRunner, NoopTerminalHook)
    .queue_name("agent-runs") // same string as in queue_configs
    .build();

Quick start

use std::sync::Arc;
use taquba::{Queue, object_store::memory::InMemory};
use taquba_workflow::{
    NoopTerminalHook, RunSpec, Step, StepError, StepOutcome, StepRunner, WorkflowRuntime,
};

struct EchoRunner;

impl StepRunner for EchoRunner {
    async fn run_step(&self, step: &Step) -> Result<StepOutcome, StepError> {
        Ok(StepOutcome::Succeed { result: step.payload.clone() })
    }
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let store = Arc::new(InMemory::new());
    let queue = Arc::new(Queue::open(store.clone(), "demo").await?);

    let runtime = WorkflowRuntime::builder(queue, store, EchoRunner, NoopTerminalHook).build();

    let worker = runtime.clone();
    tokio::spawn(async move { worker.run(std::future::pending::<()>()).await });

    let handle = runtime.submit(RunSpec {
        input: b"hello".to_vec(),
        ..Default::default()
    }).await?;
    println!("submitted run {}", handle.run_id);
    Ok(())
}

Examples

cargo run -p taquba-workflow --example single_step
cargo run -p taquba-workflow --example multi_step
cargo run -p taquba-workflow --example crash_resume
cargo run -p taquba-workflow --example fanout_jobs
ANTHROPIC_API_KEY=... cargo run -p taquba-workflow --example rig_agent
OPENAI_API_KEY=...    cargo run -p taquba-workflow --example rig_agent

crash_resume runs on the local filesystem and is the one example that demonstrates recovery directly: start it, interrupt it during any stage and start it again. The second process resumes the same run, skips the stages already committed and serves the completed units of the interrupted stage from the memo store.

rig_agent is a two-stage AI agent (research, then write) structured for between-step durability: step 0's research is persisted as queue state before step 1 begins, so on a persistent store a process that crashes between the steps resumes at step 1 without re-running the research. The example itself runs on the in-memory store; substitute a persistent object_store backend, as crash_resume does, to observe recovery across a process restart.

fanout_jobs composes the runtime with taquba-jobs for fan-out inside one run: a step submits one typed job per URL to a shared JobRunner, joins the typed results, and memoizes the aggregate so a step retry does not re-submit the fan-out.

Step outcomes

StepOutcome::Fail / StepOutcome::Cancel vs Err(StepError::permanent): runner verdicts ack normally; an infrastructure error dead-letters so operators can find it via queue.dead_jobs().

Cancellation

Call WorkflowRuntime::cancel(run_id) to cancel an active run from outside the runner:

• If the current step is pending or scheduled, the queued step job is removed and the terminal hook fires from the cancel call before it returns.

• If the current step is running, cancellation is delivered via Step::cancel_token (a tokio_util::sync::CancellationToken). Runners that watch the token can short-circuit immediately:

  tokio::select! {
      out = call_llm(step) => out,
      _ = step.cancel_token.cancelled() => {
          Ok(StepOutcome::Cancel { reason: "cooperative".into() })
      }
  }
  

  Runners that ignore the token are allowed to run to completion (futures cannot be safely aborted mid-step). In both cases the runner's StepOutcome is discarded, any pending transient retry is suppressed, and the worker fires the terminal hook with Cancelled once the step returns. Watching the token only reduces cancellation latency for slow steps; it doesn't change semantics.

While termination is in flight, WorkflowRuntime::status reports a RunState::Cancelling overlay until the entry is dropped.

Returns Ok(false) if the run is unknown or already terminal in this runtime. cancel only reaches runs submitted to this WorkflowRuntime instance; a second runtime in the same process (sharing the queue) maintains its own registry.

Durable signals

A step can pause the rest of its run until an external event. Returning StepOutcome::continue_on_signal (a Trigger::OnSignal) defers the next step until a signal for the chosen correlation key arrives via WorkflowRuntime::signal, or until the timeout elapses. The next step reads Step::signal: Some(payload) when a signal arrived, None when the timeout fired. The natural fit is a run that waits for an approval, a webhook callback or another run's completion, with the timeout as the escalation path.

// In the runner: pause the run for the payment webhook, or escalate
// after seven days.
Ok(StepOutcome::continue_on_signal(
    order_id.into_bytes(),
    format!("payment:{order_id}"),
    Duration::from_secs(7 * 24 * 3600),
))

// In the webhook handler (same process):
match runtime.signal(&format!("payment:{order_id}"), body).await? {
    SignalOutcome::Delivered => { /* a waiting run was woken */ }
    SignalOutcome::Buffered => { /* held for the next waiter */ }
}

Signals are durable in both directions. The waiting step is a scheduled job in the store, so the wait survives restarts and costs nothing while pending. A signal with no registered waiter is buffered durably under its correlation key and consumed by the next waiter registered for it, so a signal that arrives before its waiter is not lost; WorkflowRuntime::clear_signal discards a buffered signal that is no longer wanted.

Semantics: delivery follows the crate's at-least-once model (the woken step can be redelivered and observes the same Step::signal value on every attempt). One buffered signal is held per correlation key; a second signal before consumption replaces the first. One waiter is allowed per correlation key; registering a second one fails that run, so choose keys unique to the waiter (include the run id if uniqueness is uncertain). Signals are scoped to the store: the signaller is the same process that hosts the runtime, per the single-process design.

See examples/signals.rs for a runnable approval flow covering all three delivery paths (signal, timeout, buffered).

Reserved headers

Step jobs reserve the workflow.* prefix; submission rejects user headers starting with it. Other headers on RunSpec::headers thread through every step and reach the terminal hook on RunOutcome::headers.

Idempotency

Each step is enqueued with dedup_key = "run:{run_id}:{step_number}", preventing concurrent duplicate steps. But Taquba is at-least-once: a step can be claimed and executed twice if its lease expires before ack. StepRunner implementations must be idempotent for the same (run_id, step_number).

Memoizing within-step side effects

Because retries can re-execute a step, expensive non-idempotent side effects (LLM calls, paid APIs, multi-stage processing) need a place to record their result so retries observe the cached value instead of paying twice. Step::memo is a per-step durable key-value store scoped to (run_id, step_number):

// Inside StepRunner::run_step:
if let Some(cached) = step.memo.get("draft").await? {
    return Ok(StepOutcome::Succeed { result: cached });
}
let draft = expensive_call(&step.payload).await?;
step.memo.put("draft", &draft).await?;
Ok(StepOutcome::Succeed { result: draft })

When the natural memo key is the content of an input value, Memo::content_get and Memo::content_put serialize that input as MessagePack, hash it with SHA-256, and use the digest as the memo key:

#[derive(serde::Serialize)]
struct DraftInput<'a> {
    operation: &'static str,
    payload: &'a [u8],
}

let input = DraftInput {
    operation: "draft",
    payload: &step.payload,
};
if let Some(cached) = step.memo.content_get(&input).await? {
    return Ok(StepOutcome::Succeed { result: cached });
}
let draft = expensive_call(&step.payload).await?;
step.memo.content_put(&input, &draft).await?;
Ok(StepOutcome::Succeed { result: draft })

Content-addressed memo keys remain scoped to (run_id, step_number); they are not a cross-run cache. If multiple logical operations may receive identical inputs, include an operation name in the serialized input.

Memo entries live in the object store passed to WorkflowRuntime::builder under the path prefix configured by WorkflowRuntimeBuilder::memo_prefix (default "workflow-memo"). Memo is strictly per-step; the durable channel between steps is StepOutcome::Continue's payload, not memo.

Step-output replay

WorkflowRuntimeBuilder::step_output_replay enables an additional runtime-managed replay record for every outcome the runner returns, including Fail and Cancel. Step errors (StepError) are not recorded, so retries still invoke the runner. The record is keyed by (run_id, step_number, SHA-256(step payload)) and is written before the runtime applies the outcome. If the same step is delivered again after a crash before ack, the stored outcome is replayed without invoking the runner again. A replayed Continue with a Trigger::After delay reduces the delay by the time already elapsed since the outcome was stored, preserving the original schedule.

This is disabled by default because it adds one object-store read per step delivery (the replay lookup) plus one write per recorded outcome, and makes that write part of step settlement. The replay records are scoped to one run and step; they are not a cross-run cache. They are cleared with the run's memo entries when memo retention is configured.

Memo retention

By default memo entries are retained indefinitely (appropriate for short-lived runs or workloads that manage cleanup externally). To enable automatic cleanup, configure a retention window via WorkflowRuntimeBuilder::memo_retention:

let runtime = WorkflowRuntime::builder(queue, store, runner, hook)
    .memo_retention(Duration::from_secs(24 * 60 * 60))
    .build();

When retention is set, the runtime writes a small terminal marker for every terminal state (Succeeded, Failed, Cancelled) and WorkflowRuntime::run spawns a background sweeper that lists those markers and clears the memo entries, step-output replay entries, and marker for any run whose marker is older than the retention window. The first sweep fires on startup so a restarted process catches markers left behind by an earlier one.

Because the sweep is keyed on those terminal markers, and a terminated run never resumes, it never deletes the memo or replay entries of an in-flight run that a resume may still read. A resuming step that finds an entry absent re-executes the work (delivery is at-least-once regardless), so a missing entry is always safe to observe rather than a dangling reference: deletion is left unguarded precisely because every reader tolerates absence.

Advanced cleanup policies (selective retention, externally-driven sweeps) can be built directly on MemoStore::list_terminal_markers, MemoStore::clear_memos_for_run, and MemoStore::delete_terminal_marker without configuring WorkflowRuntimeBuilder::memo_retention.

Time injection

Every timestamp the runtime writes (the submitted_at_ms on the durable per-run record, the run_at it computes when a step continues with a Trigger::After delay, and the terminal-marker timestamps the memo-retention sweep consumes) is read through a taquba::Clock rather than SystemTime::now(). By default the runtime inherits the clock its Queue was opened with, so passing a MockClock to OpenOptions::clock virtualises both the queue and the workflow runtime in lockstep:

let clock = MockClock::new(1_700_000_000_000);
let opts = OpenOptions {
    clock: Arc::new(clock.clone()),
    ..OpenOptions::default()
};
let queue = Queue::open_with_options(store.clone(), "db", opts).await?;
let runtime = WorkflowRuntime::builder(queue, store, runner, hook).build();
// `runtime` reads the same clock as `queue`; `clock.advance(...)`
// moves every time-based decision the runtime makes.

Override the inherited default via WorkflowRuntimeBuilder::clock when a test or specialised setup needs the runtime on a different time source than the queue. The common case for production callers is to leave the default and let the queue's SystemClock flow through.

This makes downstream tests deterministic: Trigger::After delays, memo-retention sweep eligibility, and terminal-marker ages all advance under explicit MockClock::advance calls rather than wall-clock waits.

Duplicate submissions

WorkflowRuntime::submit is idempotent on (run_id, spec.input). A re-submission of an active run that carries the same input is a no-op and the returned SubmitOutcome has newly_submitted = false. A re-submission that carries a different input is rejected with Error::InputMismatch: reusing a run_id with new content is a programmer error; choose a fresh run_id for a new run.

Duplicates are caught from two sources, in order:

1. An in-process registry catches duplicates within the same runtime.
2. A durable per-run record written atomically with the step-0 enqueue (via Taquba's enqueue_with_kv) catches duplicates across process restarts, even after step 0 has been claimed and its dedup key released. The record carries a SHA-256 of the original input so the cross-restart mismatch check works even when the in-memory registry is empty. The record is cleaned up when the run reaches a terminal state.

Terminal hook

TerminalHook::on_termination fires once per run on Succeeded, Failed, or Cancelled, receiving the submitter's headers and the runner's result or error. WebhookTerminalHook (behind the webhooks feature) fires HTTP callbacks via taquba-webhooks; set the per-run URL on RunSpec::headers["callback_url"].

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.