aiagent.org aiagent.org Temporal.io
Durable Execution Engine

Temporal.io for AI Agents

Build autonomous AI agents that never lose state. Eliminate crash-prone HTTP timeouts and transient failure loops using Temporal's event-sourced durable execution, deterministic workflow replays, and human-in-the-loop approvals.

1 Core Architectural Concepts

Durable Execution

Code execution state (variables, call stack, timers) is persisted automatically across network outages, container restarts, and database failovers without manual checkpoints.

Workflows vs. Activities

Workflows orchestrate logic deterministically. Activities execute non-deterministic operations (calling LLM APIs, DB writes, scraping) with automated retry policies.

Deterministic Replay

When a worker crashes, a new worker reconstructs identical state by replaying past events from the append-only event history log, skipping already executed activities.

Signals & Human Approval

Workflows can sleep asynchronously for hours or days waiting for an external Signal (e.g. human manager approving a $10,000 refund) without holding active server memory.

2 Quickstart & Local Environment

Temporal CLI Dev Server

# Start local server with Web UI
temporal server start-dev
# Web UI available at:
# http://localhost:8233

Python SDK Installation

# Terminal
pip install temporalio openai

3 Production Python: Multi-Step Agent Workflow

Durable Agent with Retries & Human Gate

# agent_workflow.py
from datetime import timedelta
from temporalio import workflow, activity
# 1. Non-deterministic tool activity with automatic backoff
@activity.defn
async def execute_llm_step(prompt: str) -> str:
return "Generated action plan"
# 2. Durable agent workflow
@workflow.defn
class AutonomousAgentWorkflow:
@workflow.run
async def run(self, goal: str) -> str:
# Execute activity with exponential retries
plan = await workflow.execute_activity(
execute_llm_step, goal,
start_to_close_timeout=timedelta(minutes=2)
)
return plan

4 FAQ

Why can't I call LLMs directly inside a Workflow function? →
Workflows must be strictly deterministic so Temporal can reconstruct state by replaying past events. Calling an LLM, generating a UUID, or querying a clock inside a Workflow function breaks replay determinism. Non-deterministic operations must always be wrapped in an Activity.
How does Temporal differ from LangGraph checkpointing? →
LangGraph checkpoints state to a database after each graph node. Temporal operates at the execution runtime level — it provides full event sourcing, task queues, worker autoscaling, rate limiting, and heartbeats. Many production teams wrap LangGraph subgraphs inside Temporal activities.