# Spring AI vs OpenAI SDK vs CrewAI vs LangChain/LangGraph — Full Feature Comparison
Java developers entering the AI space face a growing landscape of frameworks. Spring AI positions itself as *the* Spring-native solution, but how does it actually stack up against the established players? This comparison covers four major approaches: Spring AI (Java/Spring), OpenAI SDK (Java thin client), CrewAI (Python multi-agent), and LangChain/LangGraph (Python orchestration). For Java developers, we also reference LangChain4j — the idiomatic Java alternative to LangChain — where relevant.
Quick Overview
| Spring AI | OpenAI Java SDK | CrewAI | LangChain / LangGraph | |
|---|---|---|---|---|
| Language | Java (Spring Boot 3.5+/4.x) | Java 8+ | Python ≥3.10 | Python (LangChain4j for Java) |
| License | Apache 2.0 | Apache 2.0 | MIT | MIT |
| Version | 1.1.5 (stable) / 2.0 (milestone) | 4.34.0 | 1.14.4 | 0.4.x / 1.0.x |
| Core purpose | Spring-native AI integration platform | Thin REST API wrapper for OpenAI | Multi-agent automation framework | Low-level stateful agent orchestration |
| Abstraction level | High (auto-config, starters, advisors) | Very low (raw API mapping) | High (YAML agents, crews, flows) | Low-mid (state graphs) to High (LangChain chains) |
| Multi-provider | ✅ 20+ LLM + 20+ Vector DBs | ❌ OpenAI only | ✅ Any via LiteLLM | ✅ 20+ LLMs |
| Spring Boot native | ✅ First-class | Starter available | ❌ | Spring Boot via LangChain4j |
Feature Comparison Table
🔵 Model & Chat APIs
| Feature | Spring AI | OpenAI SDK | CrewAI | LangChain/LangGraph |
|---|---|---|---|---|
| Chat Completion | ChatClient (fluent, WebClient-like) | client.chat().completions().create() | LLM.call() or Agent.llm | ChatOpenAI / ChatModel.invoke() |
| Streaming | ChatClient.prompt().stream() | Native SSE via OkHttp | Built-in streaming | Built-in .stream() |
| Multi-model provider | Anthropic, OpenAI, Google, Ollama, Mistral, DeepSeek, Groq, NVIDIA, Bedrock, VertexAI + more | OpenAI only | Any via LiteLLM (100+ providers) | ~20 providers (via LangChain integrations) |
| Structured Output | StructuredOutputConverter → POJO + Native JSON Schema mode | response_format param (JSON Mode) | output_pydantic / output_json | with_structured_output() (Pydantic) |
| Image Generation | Stability, Azure OpenAI, OpenAI, ZhiPu | client.images().generations() | DALL-E integration | DALL-E / Stability via chains |
| Audio (TTS/STT) | TTS (OpenAI, ElevenLabs, Azure) + Transcription (OpenAI, Azure) | client.audio() — speech + transcription | Via custom tools | Via integrations |
| Moderation | OpenAI, Mistral | client.moderations() | ❌ Not built-in | ❌ Not built-in |
Key difference — what Spring AI calls ChatClient, LangChain4j calls AI Services, and LangChain calls ChatModel. The OpenAI SDK has no abstraction layer at all — you talk directly to the REST API.
🟢 Function Calling / Tools
| Feature | Spring AI | OpenAI SDK | CrewAI | LangChain/LangGraph |
|---|---|---|---|---|
| Tool definition | @Tool annotation on methods + ToolCallback | Manual JSON schema in params | @tool decorator or BaseTool | @tool decorator / StructuredTool |
| Dynamic tools | MCP (Model Context Protocol) | ❌ Manual only | MCP integration (DSL + transport) | Built-in tool binding |
| Tool execution | Automatic by framework | You handle the loop yourself | Automatic within agent | Automatic within agent executor |
| MCP Client | ✅ Native (STDIO, SSE, Streamable HTTP) | ❌ | ✅ MCP via stdio/SSE/HTTP | ❌ Not natively |
| MCP Server | ✅ MCP Server Boot Starters | ❌ | ❌ | ❌ |
Spring AI uniquely offers MCP Server support — you can expose your own Spring services as MCP tools. No other framework in this comparison provides this.
🟠 Agents & Orchestration
| Feature | Spring AI | OpenAI SDK | CrewAI | LangChain / LangGraph |
|---|---|---|---|---|
| Single agent | Via ChatClient + Tools + Advisors | Manual loop | Agent(role=..., goal=...) | LangChain AgentExecutor / create_react_agent() |
| Multi-agent | ❌ No native multi-agent (community patterns) | ❌ | ✅ Core feature — Crews with roles, tasks, processes | LangGraph subgraphs / StateGraph |
| Agent roles | Annotation-based @Tool binding | N/A | YAML-defined (agents.yaml: role, goal, backstory) | Programmatic state + nodes |
| Task orchestration | Advisors API (call chain wrappers) | Manual | Tasks with dependencies, async, hierarchical, sequential | StateGraph with nodes and edges |
| Human-in-the-loop | ❌ Not built-in | ❌ | ✅ human_input=True per task | ✅ interrupt() in graph nodes |
| Durable execution | ❌ | ❌ | ✅ Checkpointing | ✅ Core feature — resumes from failures |
CrewAI and LangGraph are purpose-built for agent orchestration. Spring AI is closer to LangChain4j — excellent for single-agent patterns but multi-agent is an emerging area.
🔴 RAG (Retrieval-Augmented Generation)
| Feature | Spring AI | OpenAI SDK | CrewAI | LangChain / LangGraph |
|---|---|---|---|---|
| Vector stores | 20+ providers (PGVector, Pinecone, Chroma, Milvus, Redis, Elasticsearch, Neo4j…) | ❌ No RAG support | ✅ Knowledge Base (files, URLs, strings) | 30+ vector stores via LangChain integrations |
| Portable vector API | Unified VectorStore interface + SQL-like metadata filters | N/A | N/A | LangChain VectorStore abstraction |
| ETL Pipeline | ✅ Built-in document ingestion (PDF, TXT, JSON, HTML…) → split → embed → store | ❌ | ❌ | ✅ LangChain document loaders + splitters |
| Chat Memory | ChatMemory with multiple backends | Manual (you append history) | ✅ Agent memory (short/long-term) | ✅ BaseChatMemory + LangGraph long-term memory |
| Advisors (RAG wrappers) | ✅ QuestionAnswerAdvisor, VectorStoreChatMemoryAdvisor, ClearContextAdvisor… | ❌ | Knowledge search tool | LangChain RAG chains |
Spring AI’s “Advisors” are a unique abstraction — reusable patterns that wrap LLM calls. LangChain equivalent would be “Runnable chains” or “middleware.” The OpenAI SDK offers none of this.
🟣 Observability & DevOps
| Feature | Spring AI | OpenAI SDK | CrewAI | LangChain / LangGraph |
|---|---|---|---|---|
| Observability | Micrometer metrics, OTLP traces | Basic HTTP logging | 15+ tracing integrations (Langfuse, Weave, Datadog, MLflow…) | LangSmith (paid) |
| Model evaluation | ✅ Built-in ModelEvaluation + LLM-as-a-Judge | ❌ | ❌ | ✅ LangSmith evals |
| Testing | Testcontainers + @SpringAiTest | Standard unit tests | Standard Python tests | Standard Python tests |
| Spring Boot autoconfig | ✅ Starters on start.spring.io | Spring Boot starter available | ❌ | Via LangChain4j |
| Deployment | Any JVM environment (K8s, Cloud Foundry, etc.) | Any JVM | CrewAI Cloud / self-hosted | LangSmith Deployments / self-hosted |
Terminology Mapping — “Same Feature, Different Name”
| Concept | Spring AI | LangChain4j (Java) | LangChain (Python) | CrewAI | OpenAI SDK |
|---|---|---|---|---|---|
| High-level chat API | ChatClient | AI Services | ChatModel / LLMChain | Agent.llm / LLM.call() | chat().completions() |
| Reusable call wrappers | Advisors | AI Services aspects | Runnables / Middleware | Hooks (LLM Call, Tool Call) | N/A |
| Structured → POJO | Structured Output Converter | Output Parsers | with_structured_output() | output_pydantic | response_format |
| Function calling | @Tool annotation | @Tool annotation | @tool decorator | @tool decorator | JSON schema in params |
| Tool registration | ToolCallback | ToolSpecification | BaseTool | BaseTool | Manual |
| Memory | ChatMemory | ChatMemory | BaseChatMemory | Agent Memory | You manage history |
| Document ingestion | ETL Pipeline | Ingestion (DocumentLoaders) | Document Loaders | Knowledge | N/A |
| Search in vector DB | VectorStore.similaritySearch() | EmbeddingStore.search() | VectorStore.similarity_search() | Knowledge query | N/A |
| Prompt templates | PromptTemplate | PromptTemplate | PromptTemplate | YAML string templates | N/A |
| Observability | Micrometer | Micrometer | LangSmith / Callbacks | Tracing integrations | Basic HTTP logs |
What’s Missing Where
What Spring AI is missing (compared to Python frameworks):
What OpenAI Java SDK is missing (compared to all others):
What CrewAI is missing (compared to Spring AI):
What LangChain/LangGraph is missing (compared to Spring AI):
When to Use What
| Scenario | Best choice |
|---|---|
| Java/Spring shop building AI features | Spring AI — native Spring Boot, auto-config, starters, Testcontainers |
| Calling OpenAI from Java — minimal abstraction | OpenAI Java SDK — thin, fast, trivial dependency |
| Multi-agent automation (Python) | CrewAI if you want high-level crews → LangGraph if you need precise state control |
| Full-stack RAG in Java | Spring AI (built-in ETL + VectorStore abstraction) or LangChain4j (more mature, 30+ vector stores) |
| Stateful, long-running agents with failure recovery | LangGraph — durable execution is its signature feature |
| Exposing your services as MCP tools | Spring AI — MCP Server Boot Starters are unique |
| Quick prototype with agents | CrewAI — YAML config, CLI scaffolding, one-command setup |
Conclusion
Spring AI is the best fit for Java/Spring teams who want Spring-native AI integration with portability across 20+ model providers, built-in RAG, ETL pipelines, and MCP support. It’s not trying to be CrewAI or LangGraph — it’s building the Spring ecosystem’s AI layer, similar to what LangChain4j does for the broader Java world.
OpenAI Java SDK is only for direct OpenAI API calls — use it if that’s all you need, but don’t expect an AI framework.
CrewAI dominates multi-agent Python automation with its YAML-driven Crews and event-driven Flows. LangGraph is the choice when you need precise, durable, stateful orchestration.
For the complete comparison, LangChain4j deserves a mention as the closest Java competitor to Spring AI — it’s more mature (started 2023), has 30+ vector stores, AI Services, and first-class Quarkus/Spring Boot/Helidon integration. Spring AI’s main edge over LangChain4j is deeper Spring Boot autoconfiguration, built-in MCP client+server, the Advisors API, and native structured output converters.
*This comparison is based on Spring AI 1.1.5 / 2.0.0-M1, OpenAI Java SDK 4.34.0, CrewAI 1.14.4, and LangGraph 0.4.x as of May 2026.*