Choosing the right automation stack for AI-driven data workflows can mean the difference between robust, maintainable systems and brittle point-to-point scripts.
LangGraph and n8n approach automation differently: LangGraph is a graph-based framework for orchestrating LLM agents and multi-step reasoning, while n8n is a visual workflow and integration platform focused on connecting systems and moving data. This post compares them through the lens of a data automation team — features, technical trade-offs, pricing models, hosting & security, and how a data layer like Peliqan complements both.
Platform overview: agent graph orchestration vs workflow orchestration
LangGraph — graph-based LLM agent orchestration
LangGraph is a framework for defining node-and-edge graphs of LLM agents, tools, and memory modules. It provides abstractions to compose multi-agent workflows, conditional branching, and tool calls within a directed graph. LangGraph excels when you need to coordinate multiple AI agents and tools in complex, branching pipelines.
n8n — visual workflow & integration platform
n8n offers a node-based canvas for building integrations and automations across APIs, databases, and apps. It targets engineering-savvy teams who want the productivity of a visual builder plus the option to drop into code (Function / Code nodes). n8n excels at event-driven flows, ETL-style pipelines, schedule-based jobs, and cross-system orchestration, and it supports self-hosting for security and compliance.
Feature comparison — core differences that matter
At a glance: LangGraph focuses on orchestrating multiple AI agents and tool calls via graph definitions. n8n focuses on connecting systems, routing data, and visual orchestration. Both are extensible, but their abstractions and developer experience differ substantially.
LangGraph vs n8n (quick technical comparison)
| Feature / Concern | LangGraph | n8n (Cloud / Self-hosted) |
|---|---|---|
| Primary model | Graph SDK for agent orchestration, tool calls, and memory | Visual workflow runner — triggers, nodes, and connectors |
| Main languages / SDKs | TypeScript & Python SDKs — graph definitions | JavaScript runtime; JS/Python in Function/Code nodes |
| Typical role in stack | Multi-agent coordination, conditional logic, tool orchestration | System integration, event routing, ETL jobs, notifications |
| Integrations | Any API or tool via code — unlimited extensibility | 400+ official + community nodes — OAuth/API-ready connectors |
| UI / UX | Code-first graph definitions — best for engineers | Visual node canvas — low-code to engineer-friendly hybrid |
| Hosting | Embedded in your app or microservices; you manage infra | Self-host or n8n.cloud |
| AI capabilities | Native: multi-agent graphs, tool calls, memory, loops | Via API nodes or dedicated LLM/community integrations |
Why this comparison matters for data automation teams
Data automation teams need reliable orchestration of both AI logic and system integration. Choosing LangGraph or n8n affects where decision-making logic lives, how data flows through agents, and the operational burden. Use LangGraph when complex AI agent graphs and tool orchestration are core. Use n8n when connecting many systems, scheduling jobs, and operationalising integrations are primary needs. Often both are combined: n8n handles triggers/data motion; LangGraph handles the AI graph execution.
Pricing & cost model
| Plan type | LangGraph | n8n |
|---|---|---|
| Free / entry | Open-source SDK — free; infrastructure & model costs apply | Community self-hosted is free; n8n.cloud has free tiers & paid plans |
| Billing model | Infrastructure & model API costs (per-token or per-request) | Execution-based (per-workflow run) for n8n.cloud; self-hosted infra |
| Cost drivers | Graph complexity, model calls, tool execution | Number of executions, flow complexity, throughput & retention |
Interpretation: LangGraph costs center on infrastructure and AI/tool API spend. n8n costs center on execution volume and hosting. For AI-graph-heavy pipelines, LangGraph-driven spend often dominates; for broad integration workloads, n8n execution scaling can be the larger line item.
Ease of use
LangGraph — built for engineers
LangGraph assumes familiarity with code, graph definitions, and AI agent patterns. It gives deep control over multi-agent flows but requires engineering time to design, test, and observe execution graphs.
n8n — visual first, engineer extendable
n8n offers fast wins with visual flows, while allowing engineers to insert JS/Python for edge cases. Non-developers can assemble many automations; engineers maintain complex transforms and reusable subflows.
Ease-of-use summary
- LangGraph: graph-centric, code-heavy but powerful for orchestrating AI agents.
- n8n: lower entry barrier for integrations; still needs technical skill for complex pipelines.
Integration ecosystem
LangGraph — API & tool-first
LangGraph connects to any external tool or API via code, enabling dynamic tool invocation within agent graphs.
n8n — connectors & nodes
n8n integrations cover hundreds of SaaS apps and APIs out of the box, handling auth, webhooks, and polling triggers for rapid cross-system automation.
Integration highlights
- LangGraph: infinite extensibility via code — best when you need dynamic tool orchestration.
- n8n: rapid connection to many business systems with reusable nodes and templates.
Hosting & security
LangGraph — embedded in your app
LangGraph runs in your microservices or serverless functions; you secure model keys, tool credentials, and data in transit under your policies.
n8n — self-host or managed
n8n supports self-hosting for sensitive environments and a managed n8n.cloud for teams who want less ops overhead. Self-hosting gives control over logs, network access, and data residency.
When to pick what
- Choose LangGraph when orchestrating AI agents and tool calls must remain in your controlled environment.
- Choose n8n self-hosted when workflow data must remain under your governance, but you also want visual flows and connectors.
Customization & developer power
LangGraph — maximum control
LangGraph exposes primitives for defining agent nodes, tool integration, memory modules, and conditional graph edges. Engineers can implement complex branching, loops, and feedback-driven agent loops.
n8n — practical extensibility
n8n balances visual building with the ability to run custom JS/Python. It’s ideal for teams that want to combine pre-built connectors and bespoke transforms without building an entire integration platform from scratch.
Technical edge
- LangGraph: best for designing sophisticated multi-agent workflows and tool orchestration.
- n8n: best for end-to-end process automation and moving data between systems rapidly.
How Peliqan complements LangGraph and n8n in AI + data workflows
LangGraph and n8n are often combined: LangGraph executes agent graphs and tool calls; n8n triggers and orchestrates cross-system flows. Both tools, however, need a stable, queryable data layer to scale reliably — this is where Peliqan adds value.
When workflows become data-heavy
Data-heavy pain points include:
- High-volume document ingestion for embeddings and tool inputs
- Repeated API calls causing throttling and latency
- Complex joins, deduplication, enrichment and standardized schemas
- Unreliable ad-hoc payloads feeding agent inputs
What Peliqan provides
Peliqan becomes the data foundation so both LangGraph and n8n operate efficiently:
- 250+ connectors → unify SaaS, DBs, files and APIs into a consistent ingestion layer.
- Centralized transformations → Python/SQL pipelines for cleansing, enrichment and joins before agents consume data.
- AI readiness → RAG and Text-to-SQL patterns on clean, versioned datasets so agents query reliable sources.
- Cached, queryable warehouse → avoid repeated calls and scale embedding retrieval cost-effectively.
- Governance & lineage → schema enforcement and observability for audits and debugging.
LangGraph and n8n — with & without Peliqan
| Aspect | LangGraph or n8n Alone | With Peliqan |
|---|---|---|
| Data ingestion | Scattered per-graph or per-flow ingestion (duplicate work) | Unified ingestors & connectors; single source of truth |
| Agent inputs | Raw payloads — inconsistent & brittle | Clean, deduped, and enriched data for agent graphs |
| Transformations | Scattered inside graphs/flows (hard to maintain) | Central pipelines (Python/SQL) with reuse & testing |
| Scaling | Graphs or flows can hit API throttles and cost limits | Cached datasets + efficient retrieval for embeddings & queries |
| Observability | Limited across many graphs and repos | Unified lineage, schema history, and monitoring |
Who benefits most
- Data teams building multi-agent AI systems: LangGraph + Peliqan for reliable orchestration and data access.
- Ops teams automating cross-system flows: n8n + Peliqan to offload heavy data work from flows.
- AI/ML teams needing reproducible training & inference datasets.
- Consultancies building multi-system automations for clients with governance needs.
Examples
Multi-Agent Research Pipeline
Ingest: APIs & databases → Peliqan ingestion & embeddings → LangGraph agent graph for tool calls & reasoning → n8n triggers downstream notifications.
Customer Insights Orchestration
Trigger: Event → n8n orchestrates CRM & billing calls → push raw data to Peliqan → central pipeline normalises & enriches → LangGraph agents generate insights.
Automated Compliance Reporting
Schedule: n8n triggers nightly extract → Peliqan runs transformations & stores snapshots → LangGraph agent compiles audit summary → n8n distributes report.
In short:
- LangGraph → Best for engineering teams building sophisticated multi-agent graphs and tool orchestration.
- n8n → Best for teams needing to integrate many apps and automate cross-system processes quickly.
- Peliqan + either → The data backbone that turns brittle, payload-driven automations into scalable, auditable, and AI-ready workflows.
Conclusion
LangGraph and n8n are complementary tools. LangGraph gives you the control to orchestrate complex AI agent graphs; n8n helps you build and operationalise integrations and data flows.
For data automation teams, the pragmatic architecture is layered: use each tool where it’s strongest and rely on a dedicated data foundation (like Peliqan) to handle ingestion, transformations, caching, and governance. That approach reduces operational risk, improves agent inputs, and accelerates time-to-value for AI-driven automation.
