We want to orchestrate a workflow of LLMs and traditional services. What workflow engine is best for building hybrid AI/automation systems?

If you mean “one engine to orchestrate LLM calls alongside APIs, databases, queues, human approvals, and ordinary business logic,” I’d generally start with Temporal.

How I’d choose

Why Temporal is my default choice: it treats the workflow itself as durable application code. A workflow can call arbitrary services/LLMs, survive crashes and network failures, retry activities, pause for days waiting for a human, and resume from its persisted state.

For example:

Receive request
      │
      ▼
Validate / enrich ───────► Traditional API
      │
      ▼
LLM classification
      │
      ├── deterministic rule ──► Auto-process
      │
      └── needs reasoning
              │
              ▼
         LLM agent loop
          │       │
          │       └──► Tools / DB / APIs
          ▼
       Human approval
              │
              ▼
       Execute business action
              │
              ▼
        Audit / notify

Where LangGraph changes the answer

If the LLM agent is the core of the system—for example, a multi-agent researcher that loops, calls tools, maintains state, and decides dynamically what to do next—I'd strongly consider LangGraph. It is specifically designed for long-running, stateful agent workflows and supports durable execution, persistence, streaming, and human-in-the-loop control.

A particularly powerful architecture is actually:

Temporal = outer business-process orchestration LangGraph = inner agent orchestration

That separates concerns nicely: Temporal guarantees that the overall business process survives failures and waits, while LangGraph handles the inherently iterative/agentic portion.

When I'd choose n8n instead

If your priority is connecting lots of SaaS/API systems quickly, with a visual workflow editor and relatively little custom infrastructure, n8n is compelling. It explicitly combines traditional automation with AI agents, human approvals, code, and hundreds of integrations.

So my practical rule of thumb is:

• Mission-critical backend workflows → Temporal
• Complex agent reasoning → LangGraph
• Visual business automation/integrations → n8n
• Deeply AWS-native → Step Functions

If you're building a new production AI platform, I'd lean toward Temporal + LangGraph, rather than trying to make the LLM framework itself serve as your entire enterprise workflow engine.

Why Temporal is my default choice: it treats the workflow itself as durable application code. A workflow can call arbitrary services/LLMs, survive crashes and network failures, retry activities, pause for days waiting for a human, and resume from its persisted state.

For example:

Receive request
      │
      ▼
Validate / enrich ───────► Traditional API
      │
      ▼
LLM classification
      │
      ├── deterministic rule ──► Auto-process
      │
      └── needs reasoning
              │
              ▼
         LLM agent loop
          │       │
          │       └──► Tools / DB / APIs
          ▼
       Human approval
              │
              ▼
       Execute business action
              │
              ▼
        Audit / notify

Where LangGraph changes the answer

If the LLM agent is the core of the system—for example, a multi-agent researcher that loops, calls tools, maintains state, and decides dynamically what to do next—I'd strongly consider LangGraph. It is specifically designed for long-running, stateful agent workflows and supports durable execution, persistence, streaming, and human-in-the-loop control.

A particularly powerful architecture is actually:

Temporal = outer business-process orchestration LangGraph = inner agent orchestration

That separates concerns nicely: Temporal guarantees that the overall business process survives failures and waits, while LangGraph handles the inherently iterative/agentic portion.

When I'd choose n8n instead

If your priority is connecting lots of SaaS/API systems quickly, with a visual workflow editor and relatively little custom infrastructure, n8n is compelling. It explicitly combines traditional automation with AI agents, human approvals, code, and hundreds of integrations.

So my practical rule of thumb is:

Orchestrating a hybrid system—where non-deterministic LLM reasoning meets deterministic traditional microservices, databases, and APIs—usually requires a two-tier architecture . No single tool is natively a silver bullet for both deep cognitive looping and rock-solid enterprise durability.

The "best" approach depends on where your primary complexity lies: heavy business logic/durability vs. complex agentic reasoning.

Top Contenders by Architecture Type

1. The Heavyweight Durable Backbone: Temporal

• Best for: Mission-critical reliability, long-running processes, complex retries, and coordinating traditional microservices alongside AI calls.
• How it works: Temporal treats workflows as code with durable state. It guarantees that steps execute even if servers crash mid-process.
• The Hybrid Fit: You treat the LLM call as an external activity. If an LLM hallucinates an invalid JSON or the API times out, Temporal handles the retries, timeouts, and compensation logic natively.
• Trade-off: Writing non-deterministic LLM control loops directly inside Temporal requires workarounds (like worker versioning or patch markers) because Temporal expects strict code determinism.

2. The Native Agentic Graph: LangGraph

• Best for: Complex multi-agent reasoning, cyclic loops, dynamic branching, and human-in-the-loop validation.
• How it works: Models your AI workflow as a state machine with explicit nodes and edges. It handles memory persistence and checkpoints automatically across LLM steps.
• The Hybrid Fit: Nodes within your LangGraph can call traditional REST APIs, execute Python code, or query SQL databases.
• Trade-off: It is not designed to act as an enterprise system of record or handle multi-month, long-running durable background jobs across infrastructure deployments.

3. The Low-Code/Ops Glue: n8n (or Workato)

• Best for: Internal operations, rapid prototyping, connecting SaaS tools (Slack, Jira, Salesforce) with light AI augmentation.
• How it works: Visual node-based workflow builder with robust native AI nodes.
• The Hybrid Fit: Excellent for getting a hybrid pipeline running in hours rather than weeks (e.g., Webhook -> Traditional DB check -> LLM summarization -> Slack alert).
• Trade-off: Struggles under rigorous CI/CD pipelines, complex unit-testing, heavy code reviews, and extreme scale.

The Industry Consensus: The Hybrid-Hybrid Pattern

Mature engineering teams rarely force one engine to do both jobs. Instead, they pair a durable orchestrator with an agent framework:

1. Temporal (The Outer Ring): Manages the macro-workflow, authentication, database transactions, retries, and integration with traditional services.
2. LangGraph (The Inner Ring): Invoked inside a Temporal activity when the system needs to execute a volatile, multi-step LLM reasoning loop or agent handoff.

LangGraph — the most credible of the agent-graph frameworks: explicit state machines over LLM steps, checkpointing, human-in-the-loop interrupts. Right when your application logic is genuinely branchy and model-driven.

n8n is the better choice if you need to connect multiple services, move data between them, and trigger processes automatically based on events.

To help narrow down the best specific pattern, tell me:

• Are you building an internal operational tool or a customer-facing software product?
• Do you expect workflows to run for seconds/minutes or days/weeks?
• What is your primary technology stack (Python, TypeScript, Go)?

When orchestrating a hybrid system that blends the probabilistic, non-deterministic nature of LLMs with the rigid, deterministic execution of traditional services (databases, APIs, ERPs, message queues), there is no single "silver bullet." Instead, the "best" engine depends on whether your system leans more toward code-first engineering or low-code operational automation.

The leading workflow engines for hybrid AI/automation systems break down into distinct categories:

1. Code-First & Complex State Management (Best for Engineering Teams)

• LangGraph (by LangChain):
  • Best for: Complex agentic loops, cyclic workflows, and fine-grained state management.
  • Why it fits: Unlike linear chain tools, LangGraph models your agent interactions as state machines with explicit edges and nodes. It allows you to seamlessly pause execution for a human-in-the-loop approval (crucial before a traditional service executes a write operation or payment) and resume deterministically.
• Temporal.io:
  • Best for: Bulletproof reliability, long-running processes, and heavy traditional microservices integration.
  • Why it fits: While Temporal isn't an "AI-native" framework, elite engineering teams use it to orchestrate the entire hybrid architecture. You write LLM calls inside Temporal "Activities" or "Workflows." If an external API fails or an LLM times out, Temporal handles retries, compensation logic, and state persistence with absolute programmatic precision.

2. Enterprise Integration & Low-Code/Pro-Code (Best for Operations & IT)

• n8n:
  • Best for: Visual workflow design with heavy native extensibility and self-hosting options.
  • Why it fits: n8n bridges traditional webhooks, databases, and enterprise apps with advanced AI nodes. It supports complex branching, native langchain-style sub-nodes, and custom JavaScript/Python code blocks, making it exceptionally nimble for mixing standard REST APIs with LLM data extraction.
• Workato / Zapier / Activepieces:
  • Best for: Business process automation (BPA) and SaaS connectivity.
  • Why it fits: If your "traditional services" are mostly mainstream enterprise software (Salesforce, HubSpot, Snowflake, Slack) and you want a visual builder to drop AI summarization or routing steps in between, platforms like Workato or n8n let non-developers and engineers collaborate.

3. AI Product & Evaluation Platforms (Best for Dedicated AI Features)

• Vellum AI / Stack AI:
  • Best for: Managing prompts, multi-step LLM chains, and evaluations before touching backend triggers.
  • Why it fits: These platforms excel at the AI-heavy side of the pipeline (retrieval-augmented generation, fallback models, versioning prompts), but you typically pair them via webhooks or APIs to trigger your traditional business logic elsewhere.

Community Perspectives on Hybrid Design

Developers leaning toward code-heavy architectures often emphasize separating deterministic rules from probabilistic AI choices to prevent debugging nightmares:

Community discussions highlight that leaving exit conditions entirely up to LLMs can lead to debugging challenges; implementing external checks like budget counters or typed exit states keeps orchestrations auditable. Furthermore, when steps and evaluation algorithms are clearly defined, traditional workflows outperform pure agentic approaches in cost, latency, and debuggability, with orchestration proving most valuable for truly open-ended branches. Full details and direct community quotes can be explored in the referenced discussion.

To help narrow down the ideal choice, tell me:

For a hybrid AI/automation system—where LLM agents, APIs, databases, queues, human approvals, and traditional business processes all interact—there usually is not one universal “best” workflow engine. The right choice depends on whether your hard problem is durable business process execution or AI reasoning orchestration.

A practical shortlist:

Coding Protocols Analytics Insight## My default architecture for serious hybrid AI systems

A common production pattern is:

                 User / Event
                      |
                      v
              Durable Workflow Layer
                  (Temporal)
                      |
        +-------------+-------------+
        |                           |
        v                           v
 Traditional Services          AI Agents
 APIs, DBs, queues             LangGraph
 ERP, CRM                      RAG
 Payments                      Tool calling
                              Memory
        |                           |
        +-------------+-------------+
                      |
              Human approvals
              Monitoring
              Audit logs

The reasoning:

• Temporal handles the things you cannot afford to lose:

• “Send this customer contract for review.”

• “Wait three days for approval.”

• “Retry this payment workflow.”

• “Resume after a Kubernetes node dies.”

• LangGraph (or another agent framework) handles the things that are inherently probabilistic:

• “Decide which tools to call.”

• “Gather evidence.”

• “Draft a response.”

• “Iterate until quality checks pass.”

This separation avoids making an LLM responsible for business process guarantees.

When I would choose each

Choose Temporal if:

• Workflows can run for hours, days, or weeks
• Failures must recover automatically
• You need auditability and replay
• You mix AI calls with payments, approvals, fulfillment, compliance, etc.

Example:

“An AI agent reviews a loan application, calls risk models, requests documents, waits for a human reviewer, and completes the decision process.”

Choose LangGraph if:

• The core problem is agent behavior
• You have many tool calls and branching reasoning
• You need agent memory and state transitions

Example:

“A research agent searches sources, evaluates evidence, asks follow-up questions, and produces a report.”

Choose Airflow/Prefect/Dagster if:

• Your “workflow” is really a data pipeline:

• ingest data

• transform

• embed

• train

• evaluate

• deploy

A few design principles that matter more than the engine

1. Keep LLM calls as activities, not the workflow controller

• Treat models as fallible external services.
• Add retries, timeouts, validation, and fallbacks.

2. Persist state outside the prompt

• Store workflow state, documents, decisions, and artifacts explicitly.

3. Make deterministic steps deterministic

• Calculations, permissions, billing, and compliance rules should not be delegated to an LLM.

4. Add observability early

• Track model version, prompts, tool calls, latency, cost, and human overrides.

Recommendation

For most teams building enterprise-grade hybrid AI automation today:

• Core orchestration: Temporal
• Agent reasoning layer: LangGraph or another agent framework
• Data pipelines: Airflow/Dagster/Prefect as needed
• Business-facing workflows: Camunda if BPMN/governance is a major requirement

That gives you a system where AI can be flexible without making the entire automation stack fragile.

For hybrid AI + traditional automation, my default recommendation is Temporal.

The key distinction is: use a durable workflow engine as the system of record for execution, and treat LLMs as one type of activity inside the workflow.

My ranking

1. Temporal — best overall for hybrid production systems

• Excellent for deterministic services + LLM calls + APIs + queues + human approvals.
• Durable execution means workflows survive crashes, deployments, network failures, and long waits.
• Native retries, timeouts, signals, timers, task queues, and compensation patterns.
• Particularly good when workflows may run for hours, days, or months.
• Temporal's current AI architecture explicitly recommends putting LLM calls and external I/O in Activities while the Workflow owns durable orchestration.

temporal.io 2. LangGraph — best if the workflow itself is primarily an AI agent

• Excellent for stateful, multi-step agents, branching, tool use, memory, streaming, and human-in-the-loop.
• Especially attractive when the LLM dynamically determines what happens next.
• Less naturally suited than Temporal to being the enterprise-wide orchestration backbone for arbitrary non-AI business processes.

langchain.com 3. LangGraph + Temporal — arguably the strongest architecture for sophisticated systems

This is the combination I'd seriously consider for a large platform:

               Temporal
         durable orchestration
                   │
      ┌────────────┼────────────┐
      ▼            ▼            ▼
  Traditional   LangGraph     Human
   services       agent       approval
      │            │
      │       ┌────┴────┐
      │       ▼         ▼
      │      LLMs      Tools
      │
      └───────────────┘

Temporal handles: retries, durability, scheduling, long-running state, external services, compensation, business processes, and failure recovery.

LangGraph handles: the internal agent loop—reasoning, tool selection, state transitions, multi-agent coordination, etc.

This isn't just theoretical: LangChain's own comparison now describes the distinction essentially as "Temporal executes your workflows; LangGraph builds your agents", and notes that teams can use both.

The decision rule I'd use

The important architectural insight is that "AI workflow orchestration" and "workflow orchestration" aren't necessarily the same layer. LangGraph is optimized around the former; Temporal is optimized around the latter. LangGraph itself recommends it for workflows mixing deterministic and agentic components, while Temporal is designed to make arbitrary application execution durable.

So if you are starting a platform intended to orchestrate both LLMs and traditional services, I'd start with Temporal as the backbone and add LangGraph selectively where you actually need agentic control flow. This gives you a much cleaner separation between "the business process must complete reliably" and "the AI gets to reason about how to accomplish this step."

Temporal (or its managed counterpart Temporal Cloud) and LangGraph are the best choices for building hybrid AI/automation systems, depending on whether your architecture leans heavier on deterministic microservices or stateful agentic graphs.

Comparing Top Workflow Engines for Hybrid AI

Architectural Recommendations

• Choose Temporal if your system needs to call an LLM as one step inside a larger, ultra-reliable transaction (e.g., Charge Credit Card -> Call LLM for Fraud Analysis -> Update Database -> Trigger Fulfillment ). Temporal treats the LLM call like any other flaky network service, handling retries, timeouts, and long-running waits seamlessly.
• Choose LangGraph if your core loop is agentic—meaning the LLM dynamically decides whether to call a traditional service, loop back on its own output, or pause for human approval before proceeding.
• Hybrid Pattern: Many advanced engineering teams use LangGraph to manage the internal reasoning loop of the AI agent, and wrap that entire LangGraph execution inside a Temporal workflow to guarantee that the broader business process never loses state or fails mid-operation.

If you can share:

• Whether your application is real-time / user-facing or background batch processing
• Your primary tech stack (Python, Node.js, Go)

I can help you outline a reference architecture or choose between these options.

For hybrid AI + traditional automation, my default choice today is Temporal.

Temporal is particularly strong when your workflow contains a mix of:

• LLM calls and AI agents
• REST/gRPC services
• databases and queues
• human approvals
• retries and compensating actions
• long-running processes
• scheduled/delayed work
• workflows that must survive crashes and deployments

Its key advantage is durable execution: workflow state is persisted, so if a worker, API, or downstream service fails, the workflow can resume rather than starting over. Temporal explicitly supports AI agents alongside ordinary business processes.

How I'd choose

LangGraph is excellent when you need sophisticated agent behavior—multi-agent graphs, memory, streaming, human-in-the-loop, and custom agent state transitions. Its own documentation positions it specifically for workflows mixing deterministic and agentic components.

The interesting architecture, therefore, is often Temporal + LangGraph, rather than choosing one or the other:

                    Temporal
              durable business workflow
                       │
        ┌──────────────┼──────────────┐
        │              │              │
     Payment        Database       LangGraph
      service        service       AI agent
                                      │
                              ┌───────┼───────┐
                              │       │       │
                             LLM     RAG    Tools
                                      │
                                  external APIs

Temporal handles "make sure this process reliably happens"; LangGraph handles "reason, use tools, maintain agent state, and decide what to do next." LangChain's own comparison describes essentially this division: Temporal is a durable execution engine, while LangGraph is focused on building agents.

My recommendation

If you're building an enterprise-grade hybrid automation platform, I'd start with:

Temporal as the outer orchestration/durability layer + LangGraph (or your own agent framework) inside selected AI activities.

That gives you a clean separation:

• Temporal: reliability, retries, timeouts, scheduling, state, compensation, human waits, observability of business execution.
• LLM/agent layer: prompts, tool selection, reasoning, memory, RAG, model routing.
• Traditional services: ordinary APIs/workers, completely independent of the AI stack.

One particularly important Temporal pattern is to put every LLM call, tool invocation, external API request, and database interaction in an Activity, rather than directly in the deterministic Workflow code.

If your workflows are mostly short-lived API → LLM → API automations and you want a visual builder, I'd instead look seriously at n8n. If they're mostly agentic reasoning loops, I'd start with LangGraph. But for the broad "LLMs + traditional services + reliable production workflows" problem, Temporal is the strongest foundation.

                    Temporal
              durable business workflow
                       │
        ┌──────────────┼──────────────┐
        │              │              │
     Payment        Database       LangGraph
      service        service       AI agent
                                      │
                              ┌───────┼───────┐
                              │       │       │
                             LLM     RAG    Tools
                                      │
                                  external APIs

Temporal handles "make sure this process reliably happens"; LangGraph handles "reason, use tools, maintain agent state, and decide what to do next." LangChain's own comparison describes essentially this division: Temporal is a durable execution engine, while LangGraph is focused on building agents.

My recommendation

If you're building an enterprise-grade hybrid automation platform, I'd start with:

Temporal as the outer orchestration/durability layer + LangGraph (or your own agent framework) inside selected AI activities.

That gives you a clean separation:

Building hybrid AI and automation systems requires a workflow engine that bridges two very different paradigms: nondeterministic AI reasoning (LLMs guessing, planning, and calling tools) and deterministic traditional services (APIs, databases, payment gateways, and queues that require absolute reliability, retries, and strict state).

Choosing the "best" engine depends entirely on your team's architecture, scale, and whether you prefer visual low-code building or code-first engineering.

1. Code-First & Ultra-Durable: Temporal

If your hybrid system handles mission-critical business processes (e.g., financial transactions, multi-step customer provisioning, automated operations) where a dropped state or a failed API call can break production, Temporal is the gold standard.

• How it works: You write workflows in standard code (Python, Go, TypeScript, etc.). Temporal guarantees state persistence, automatic retries, timeouts, and long-running operations.
• Why it shines for AI: You can cleanly pass an LLM prompt and a tool registry into a Temporal workflow. If an LLM call hangs or a traditional microservice goes down mid-workflow, Temporal pauses/retries durably without losing the execution history or conversational state.
• Best for: Enterprise-grade backends, high-reliability requirements, and complex engineering teams.

2. AI-Native & Graph-Based: LangGraph (by LangChain)

If your workflow is deeply agentic—meaning the LLM itself dynamically decides the routing, loops back on errors, and modifies a shared contextual state across multiple steps—then a code-first graph framework like LangGraph is ideal.

• How it works: Models your AI application as a graph of nodes (agents, tools, and traditional code functions) and edges (conditional routing based on LLM output).
• Why it shines for AI: It features built-in persistence, time-travel (the ability to rewind and replay agent steps), and human-in-the-loop interrupt patterns natively built around LLM message histories.
• Best for: Advanced multi-agent systems, complex conversational loops, and AI-first applications.

3. Visual Low-Code & Rapid Integration: n8n

If you need to connect 800+ standard SaaS applications (Jira, Slack, Salesforce, Postgres) to an LLM node rapidly without writing thousands of lines of boilerplate API glue code, n8n has emerged as a powerhouse for hybrid automation.

• How it works: A node-based, self-hostable visual workflow builder that natively supports advanced AI building blocks (LangChain integrations, vector stores, and custom tool nodes) alongside traditional webhooks and APIs.
• Why it shines for AI: It democratizes building—allowing operations and product teams to visually wire an LLM to data inputs, memory, and traditional service endpoints in minutes rather than days.
• Best for: Rapid prototyping, internal ops, IT automation, and teams leveraging a mix of technical and non-technical builders.

Quick Comparison Matrix

“I use temporal to orchestrate my agents my human interaction. and it is agentic it calls an LLM to solve these problems.”

To help narrow down the right fit, tell me:

• What is the primary objective of the workflow (e.g., customer support chatbot, automated data extraction/processing, internal operations)?
• Do you prefer a visual low-code builder or a code-first framework?
• How critical is durable state recovery if a traditional service drops mid-execution?

For hybrid systems combining LLMs and traditional microservices, Orkes Conductor and Temporal are the best code-first orchestration engines, while n8n is top for low-code/visual setups.

Choosing the right engine depends on your team's engineering style and system complexity.

Top Workflow Engines for Hybrid AI Systems

Key Considerations for Hybrid Architectures

• State & Durability: LLM APIs timeout or fail often. You need an engine like Temporal or Conductor that supports retries, long pauses, and precise state checkpointing.
• Deterministic vs. Emergent: Use traditional code for deterministic database updates and security checks, and restrict LLMs to unstructured data extraction, summarization, or dynamic routing steps.
• Human-in-the-Loop: Ensure your engine supports pausing workflows for human approval before an LLM triggers an expensive or sensitive traditional service (like a payment or database write).

To help narrow down the ideal choice, tell me:

• Are you looking for a code-first library/SDK (like Temporal/LangGraph) or a hosted orchestration platform/visual builder (like Conductor/n8n)?
• Do you need to support long-running workflows that span hours or days?

If your goal is a hybrid workflow that combines LLMs/agents with ordinary deterministic services, my default recommendation is Temporal.

The key distinction is whether your workflow engine or your agent runtime owns orchestration.

My ranking

Why I'd choose Temporal

For a system like:

Customer request
      ↓
Validate / enrich data
      ↓
LLM decision
      ↓
 ┌────┴─────┐
 ↓          ↓
CRM API   Database
 ↓          ↓
LLM review / classification
      ↓
Human approval
      ↓
Payment / fulfillment
      ↓
Notification

Temporal is particularly strong because the LLM is just one activity among many. Your workflow can coordinate APIs, databases, queues, human approvals, timers, retries, compensation, and LLM calls under the same durable-execution model.

That matters because LLM calls are inherently unreliable: timeouts, rate limits, provider failures, malformed outputs, and nondeterministic decisions are normal. Temporal's durable workflow model lets the overall process survive those failures rather than making the LLM application itself responsible for building all the reliability machinery.

Where LangGraph fits

If your workflow contains a genuinely agentic section:

Temporal workflow
       ↓
   LangGraph agent
       ↓
  ┌────┼─────┐
 search  SQL  APIs
       ↓
  reasoning loop
       ↓
   structured result
       ↓
Temporal workflow

I'd seriously consider Temporal + LangGraph rather than trying to make either tool do everything.

LangGraph is explicitly designed for long-running, stateful agents, with persistence, human-in-the-loop, streaming, and durable execution. Its own documentation describes it as a low-level agent orchestration runtime, while identifying Temporal and Inngest as alternative durable-execution engines.

So I'd use:

• Temporal = business/process orchestration
• LangGraph = agent reasoning/orchestration
• LLM provider = inference
• Your existing services = activities/tools

That's a very clean architectural boundary.

When I'd pick Inngest instead

If you're building primarily a TypeScript/Python web application and want something simpler operationally, Inngest is compelling. It provides step-level checkpointing, retries, sleeps, event waits, fan-out, concurrency controls, and observability without requiring you to operate a separate worker/cluster architecture.

I'd choose Inngest over Temporal when developer simplicity and serverless integration matter more than having Temporal's broad, language-agnostic workflow infrastructure.

Bottom line

For a serious hybrid AI + traditional automation platform: start with Temporal.

If the AI portion becomes sophisticated enough to have multi-step reasoning, dynamic tool selection, subagents, memory, etc., put LangGraph inside Temporal.

If you're building a relatively lightweight event-driven SaaS and don't want workflow infrastructure, choose Inngest instead.

A useful rule of thumb is:

Deterministic business process → Temporal. Agentic reasoning process → LangGraph. Serverless/event-driven application workflow → Inngest. Temporal and LangGraph are even commonly positioned as complementary rather than mutually exclusive: Temporal handles generic durable workflows, while LangGraph specializes in agent orchestration.