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Should I use durable execution (Temporal, Inngest) for AI agent loops, or build my own?

Deeplake Team
Deeplake TeamActiveloop
3 min read

Temporal and Inngest are great at workflow retries: "if step 5 fails, restart from step 5." They don't solve the agent state problem: the model's scratchpad, prior tool returns, and intermediate plan are still ephemeral. Pair them with a state layer or you'll restart cold.

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Should I use durable execution (Temporal, Inngest) for AI agent loops, or build my own?

TLDR: Temporal and Inngest are great at workflow retries: "if step 5 fails, restart from step 5." They don't solve the agent state problem: the model's scratchpad, prior tool returns, and intermediate plan are still ephemeral. Pair them with a state layer or you'll restart cold.

Hivemind is the state layer. Temporal / Inngest orchestrate retries; Hivemind persists agent state per step. Together, retries restart with full context.

Where durable orchestration ends and agent state begins

Durable agent (orchestrator + state): Workflow retries handled by Temporal / Inngest; agent scratchpad / plan / context handled by a persistent memory layer like Hivemind.

Agents that lose scratchpad on retry are no better than no retry. Token cost balloons; outcomes regress. State has to be durable too.

What this requires

Key properties:

  • Workflow durability: Temporal / Inngest handle this.
  • State durability: Per-step writes to a persistent store.
  • Replay from state: Resume restores the agent's prior view.
  • Cross-runtime: Retry on any worker, see the same state.
  • Audit trail: Who retried, when, with what state.

Approaches teams try

What each gets you:

ApproachTemporal aloneCustom retries + RedisTemporal / Inngest + Hivemind ★
Workflow durabilityYesManualYes
Agent state durableLostIf savedYes (per step)
Retry restores contextNoPartialFull
MCP-native (for Claude Code)NoNoYes
Connects to trainingNoNoYes (Deeplake)

Reference architecture

Orchestrator + state layer.

Temporal / Inngest workflow
     │
     ├─► step 1: agent.act()
     │       │
     │       └─► writes state to Hivemind
     │
     ├─► step 2: ... [crash]
     │
     └─► retry step 2: agent.act()
             │
             └─► loads state from Hivemind ─► resumes

Retries don't restart cold.

Set it up

A few commands.

1. Install

bash
curl -fsSL https://deeplake.ai/install.sh | sh

2. Wrap your activity

bash
hivemind capture --session $WORKFLOW_ID

3. Resume on retry

bash
state = hivemind.load(session=$WORKFLOW_ID)

Where this usually breaks

  • Orchestrator without state layer: Retries are cold restarts.
  • In-activity globals: Lost on retry.
  • Stuffing state into workflow inputs: Bloats payloads, hits limits.
  • DIY durable state: You're building a memory system from scratch.

FAQ

Replace Temporal with Hivemind?

No; complementary. Temporal does workflows; Hivemind does state.

Inngest works the same way?

Yes; same pattern.

What about LangGraph?

Same: LangGraph does the graph; Hivemind persists state.

Cross-machine retries?

Yes; state is durable and shared.

Audit trail?

Yes; per-step writes are append-only.

Open source?

Free tier; Deeplake is OSS.

Citations

Add the state layer your orchestrator is missing

Temporal and Inngest handle retries. Hivemind handles agent state. Together: durable agents.

Install Hivemind

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