Suggestions appear as you type. Use the up and down arrows to choose one and Enter to open it.

This page's audience real numbers from our own analytics — open to see them
–Visitors
–Page views
–Clicks to vendors
–Time on page
–Reading now
Clicks to vendors, by tool
  • –
Top countries
  • –
Devices
  • –

– · counted by iTechGuides's own first-party analytics, bots removed, every figure rounded down · how we count

Head-to-head · LLM Application Development Frameworks

LangGraph vs Spring AI

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score LangGraph #2 in LLM Application Development Frameworks 9.2/10 Free plan Free plan✓ 0 of 5 features Visit LangGraph
Spring AI #4 in LLM Application Development Frameworks 9.0/10 Free plan Free plan✓ 3 of 5 features Visit Spring AI

LangGraph leads on 0 checks, Spring AI on 3, and 1 is even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreLangGraph · 9.2/10
  • Free planboth
  • Most featuresSpring AI · 3 of 5

LangGraph scores higher on our rubric for llm application development frameworks: 9.2 against 9.0 out of 10; our editors rank them #2 and #4.

Spring AI offers rag support; LangGraph doesn't publish it. Spring AI offers agent and tool use; LangGraph doesn't publish it. Spring AI offers prompt optimization; LangGraph doesn't publish it.

LangGraph is the better fit for developers building durable, stateful agent workflows. Spring AI is the better fit for java teams building RAG and AI applications.

  • LangGraph fits best

    Developers building durable, stateful agent workflows

  • Spring AI fits best

    Java teams building RAG and AI applications

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. How we rank.

Side by side

Feature LangGraph 9.2/10 Visit ↗ Spring AI 9.0/10 Visit ↗
At a glance
Editor score 9.2 9.0
Ranking #2 in LLM Application Development Frameworks #4 in LLM Application Development Frameworks
Best for Developers building durable, stateful agent workflows Java teams building RAG and AI applications
Pricing model Free Free
Starting price Not published Not published
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted, Cloud Self-hosted
Integrations 2 integrations 16 integrations
Built for Solo, Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features LangGraph 0/5 · Spring AI 3/5
RAG support Not published ✓ (best)
Agent and tool use Not published ✓ (best)
Workflow graphs Not published Not published
Prompt optimization Not published ✓ (best)
JavaScript/TypeScript SDK Not published Not published
Specs
Primary language Not published Java
Open-source license Not published Apache-2.0
Our review
Pros
  • Combines deterministic code and LLM-driven steps in graph workflows
  • Supports durable execution, checkpointing, and resumption after failures
  • Offers memory, streaming, and human review of agent state
  • Portable APIs cover multiple model providers and synchronous or streaming use.
  • RAG advisors, document ETL, evaluation, and observability support end-to-end workflows.
  • Connects to a broad range of model providers and vector stores.
Cons
  • Low-level control requires developers to define workflow behavior
  • Does not prescribe a high-level agent architecture
  • Verified integrations are narrower than LangChain’s
  • Java and Spring focus makes it less suited to teams using other application stacks.
  • Self-hosted deployment puts application operation on the adopting team.
  • Framework code requires developers to assemble and maintain the application.
Our verdict

LangGraph is an open-source framework and runtime for developers building long-running, stateful agents and workflows. Its graph model combines deterministic code with model-driven steps, and supports single-agent, multi-agent, and…

Read the review →

Spring AI is an open-source Java framework for building AI-powered applications in the Spring ecosystem. It is aimed at teams that want to connect models, enterprise data, and application APIs while keeping model-provider implementations…

Read the review →
  1. LangGraphLLM Application Development Frameworks 9.2Free plan
  2. Spring AILLM Application Development Frameworks 9.0Free plan

Strengths and trade-offs

  • LangGraph — where it wins

    • Combines deterministic code and LLM-driven steps in graph workflows
    • Supports durable execution, checkpointing, and resumption after failures
    • Offers memory, streaming, and human review of agent state

    Where it doesn't

    • Low-level control requires developers to define workflow behavior
    • Does not prescribe a high-level agent architecture
    • Verified integrations are narrower than LangChain’s

  • Spring AI — where it wins

    • Portable APIs cover multiple model providers and synchronous or streaming use.
    • RAG advisors, document ETL, evaluation, and observability support end-to-end workflows.
    • Connects to a broad range of model providers and vector stores.

    Where it doesn't

    • Java and Spring focus makes it less suited to teams using other application stacks.
    • Self-hosted deployment puts application operation on the adopting team.
    • Framework code requires developers to assemble and maintain the application.

More comparisons

All LLM application development frameworks comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update