Temporal is a durable execution platform that lets developers write workflows as ordinary code — the platform guarantees that functions run to completion even through process crashes, network failures, and machine restarts.
What Problem It Solves
Long-running processes fail. A 10-step agent workflow that crashes at step 7 normally has to restart from scratch, burning tokens, time, and money. Temporal solves this by transparently persisting workflow state at every step. If a worker dies, a new worker picks up exactly where it left off — no checkpointing code, no retry logic, no state machines.
This is the same "durable execution" concept that Dapr Agents brought to Kubernetes-native AI workloads, but Temporal has been doing it since 2020 (and its predecessor, Uber's Cadence, since 2017).
Why It's in Assess
Temporal is battle-tested for general workflow orchestration — Uber, Netflix, Snap, Stripe, and Datadog run it in production. However, its application specifically to AI agent workflows is still emerging. Purpose-built agent frameworks like Dapr Agents and LangGraph offer higher-level agent abstractions (roles, tools, memory) that Temporal does not provide out of the box.
Temporal is in Assess rather than Trial because:
- It solves a real problem for production agents (durable execution, failure recovery)
- But most teams building AI agents today reach for agent-specific frameworks first
- The value proposition becomes clear when agent workflows need production reliability guarantees that lighter frameworks lack
Teams already running Temporal for microservice orchestration should evaluate it for agent workflows immediately — the infrastructure investment is already made.
When to Choose Temporal
- Long-running agent tasks that can't afford to restart on failure (multi-hour code generation, data pipeline agents)
- Regulated environments requiring audit trails and deterministic replay of every workflow step
- Polyglot teams — Temporal has official SDKs for Go, Java, Python, TypeScript, .NET, and PHP
- Teams already using Temporal for non-AI orchestration — adding agent workflows is incremental
When NOT to Use Temporal
- Simple single-turn agent interactions that don't need durable state
- Teams that want agent-specific primitives (roles, tools, memory) built in — use CrewAI or LangGraph instead
- Prototyping — Temporal's operational overhead (server cluster or Temporal Cloud subscription) is too heavy for experiments
Compared to Other Orchestration Approaches
| Temporal | Dapr Agents | LangGraph | |
|---|---|---|---|
| Durable execution | Yes (core feature) | Yes (Dapr Workflows) | No (external) |
| Agent primitives | No (general-purpose) | Yes (roles, tools) | Yes (graph nodes) |
| Language SDKs | Go, Java, Python, TS, .NET, PHP | Python | Python |
| Scale-to-zero | No | Yes (virtual actors) | No |
| Operational model | Self-hosted cluster or Temporal Cloud | Kubernetes + Dapr | In-process library |
| Production maturity | 6+ years, thousands of orgs | v1.0 (March 2026) | ~2 years |
| License | MIT | Apache 2.0 | MIT |
Key Characteristics
| Property | Value |
|---|---|
| Founded | 2019 (from Uber's Cadence project) |
| Language | Go (server), multi-language SDKs |
| License | MIT |
| Hosted option | Temporal Cloud (managed) |
| GitHub | temporalio/temporal |
| Stars | ~19K |
| Website | temporal.io |