AI Compare

LangGraph vs Dify

Compare LangGraph and Dify across everyday workflows, strengths, limitations, pricing notes, and best-fit scenarios.

Comparison notice

Comparisons explain practical trade-offs for specific product pairs. They do not declare overall winners, publish scoreboards, or invent benchmark results.

Editorial status

Published 2026-07-30 · Last reviewed 2026-07-30 · Next review due 2026-10-28

  • Review cadence: Quarterly
  • Verification badge: Verified
  • Review status: Current
  • Evidence level: editorial
  • Content owner: ONULSURI Editorial

Read the AI editorial policy

LangGraph (LangChain) and Dify (Dify) overlap enough that buyers often shortlist both when evaluating agents tools.

This comparison focuses on engineer-first graphs versus LLM app platforms. Preferences depend on your stack, review discipline, and governance needs.

ONULSURI is not affiliated with either vendor. Features and packaging change — verify current details on official product pages before purchasing.

Subjects

LangGraph (LangChain)

Graph-based stateful agent and workflow runtime in the LangChain ecosystem.

Dify (Dify)

LLM app platform for assistants, RAG, and workflow builders.

Overview

Teams comparing LangGraph and Dify usually care about fit for engineer-first graphs versus LLM app platforms, not marketing taglines.

Run the same representative tasks on both products, keep a human review step, and record where each tool saves or costs time.

Key Differences

LangGraph is oriented around: Graph-based stateful agent and workflow runtime in the LangChain ecosystem.

Dify is oriented around: LLM app platform for assistants, RAG, and workflow builders.

The practical fork is engineer-first graphs versus LLM app platforms. Confirm current feature packaging on official docs because both products iterate quickly.

Tool calling and code actions

Agents that write or run code need guardrails.

Define which actions require human approval before production writes.

Log prompts, tools, and outcomes for auditability.

Plan and prompt quality

Agent plans are drafts.

Inspect intermediate plans before long autonomous runs.

Prefer structured outputs with schemas.

Agent orchestration

How LangGraph and Dify structure multi-step work.

LangGraph: Graph-based stateful agent and workflow runtime in the LangChain ecosystem.

Dify: LLM app platform for assistants, RAG, and workflow builders.

Compare failure recovery on a fixed automation brief.

State and memory

Debugging traces matter more than demo magic.

Prefer stacks where you can inspect steps and intermediate state.

Unbounded runs raise silent-error risk.

Pricing overview

Open-source vs hosted packaging differs.

Include hosting, model tokens, and ops time when comparing cost.

Confirm commercial terms where a vendor hosts the stack.

Orchestration fit

engineer-first graphs versus LLM app platforms

Decision hinge: engineer-first graphs versus LLM app platforms.

Pilot on one production-adjacent workflow with rollback.

Availability

Self-host vs SaaS trade-offs.

Confirm deployment model for LangGraph and Dify.

Self-hosting shifts reliability ownership to your team.

Strengths

LangGraph strengths

  • Graph-based stateful agent and workflow runtime in the LangChain ecosystem.
  • Clearer fit when your workflow matches: engineer-first graphs versus LLM app platforms
  • Accelerates first drafts when humans keep publish/merge authority

Dify strengths

  • LLM app platform for assistants, RAG, and workflow builders.
  • Strong peer alternative when evaluating the same job: engineer-first graphs versus LLM app platforms
  • Supports side-by-side pilots with shared acceptance checks

Weaknesses

LangGraph limitations

  • Outputs still need human verification for accuracy and policy fit
  • Feature and pricing packaging can change — re-check official docs
  • Weaker fit if you need Dify's primary strength: LLM app platform for assistants, RAG, and workflow builders.

Dify limitations

  • Same verification burden as other AI tools
  • Governance, permissions, and data-handling review still required
  • Weaker fit if you need LangGraph's primary strength: Graph-based stateful agent and workflow runtime in the LangChain ecosystem.

Best for

LangGraph is often a better fit when

  • Your primary workflow matches: Graph-based stateful agent and workflow runtime in the LangChain ecosystem.
  • You can supervise outputs with existing review practices
  • Your stack already invests in its ecosystem or deployment model

Dify is often a better fit when

  • Your primary workflow matches: LLM app platform for assistants, RAG, and workflow builders.
  • You want a distinct alternative for the same evaluation tasks
  • Procurement or architecture constraints favor its deployment model

Decision guidance

Choose between LangGraph and Dify based on engineer-first graphs versus LLM app platforms, not on absolute rankings.

Recommended workflow: pick 3–5 real tasks, run both tools, measure review time and defect rate, then standardize only after a successful pilot.

Keep related ONULSURI tool overviews, pricing notes, and guides handy so the decision stays evidence-based as products change.

FAQ

Is LangGraph better than Dify?

No universal ranking is claimed. Better depends on your workflow, supervision model, and constraints. Pilot both on identical tasks.

What is the main difference between LangGraph and Dify?

The practical difference emphasized here is engineer-first graphs versus LLM app platforms. Confirm current packaging on official docs.

How should teams compare pricing?

Use official pricing pages and compare against real monthly usage. This overview stays qualitative because tiers change frequently.

Do these tools replace human review?

No. For LangGraph vs Dify, treat outputs as drafts. Keep tests, editorial review, or compliance checks before publish or merge.

Where can I read more on ONULSURI?

Follow related comparisons, tool detail pages, and guides linked from this article for adjacent options and selection checklists.

  • AI Hub — Overview of ONULSURI AI guides and where each section fits.
  • AI Tool Directory — Category directory and tool overviews.
  • AI Pricing — Plan structure and upgrade guidance without fabricated prices.
  • AI Benchmarks — Transparent evaluation frameworks and scenario suites.
  • Prompt Library — Reusable prompts for coding, writing, and everyday work.
  • AI Guides — Evergreen topic guides for choosing tools and workflows.