{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"spring-ai","slug":"spring-ai","name":"Spring AI","type":"framework","url":"https://github.com/spring-projects/spring-ai","page_url":"https://unfragile.ai/spring-ai","categories":["frameworks-sdks","rag-knowledge","data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"spring-ai__cap_0","uri":"capability://tool.use.integration.provider.agnostic.chat.model.abstraction.with.unified.api","name":"provider-agnostic chat model abstraction with unified api","description":"Spring AI provides a unified ChatModel and StreamingChatModel interface that abstracts away provider-specific implementations (OpenAI, Azure, Anthropic, Vertex AI, Ollama, Bedrock). Developers write once against the Spring AI interface and swap providers via configuration properties without code changes. The framework handles protocol translation, authentication, and response normalization internally, enabling true portability across 8+ LLM providers.","intents":["I want to build an LLM app that isn't locked into one provider and can switch vendors without refactoring","I need to support multiple LLM providers in the same application for cost optimization or failover","I want to test my AI logic against different models without rewriting integration code"],"best_for":["Enterprise Java teams building multi-tenant AI applications","Organizations evaluating multiple LLM providers before committing","Teams migrating from one provider to another"],"limitations":["Provider-specific features (e.g., OpenAI's vision_detail parameter) require custom ChatOptions subclasses, breaking abstraction","Response streaming behavior varies subtly between providers; normalization adds ~50-100ms latency","No automatic fallback or load-balancing across providers — requires external orchestration"],"requires":["Java 17+","Spring Framework 6.0+","Spring Boot 3.0+ (for auto-configuration)","Valid API credentials for at least one supported provider"],"input_types":["text prompts","message lists with role/content","structured chat options (temperature, max_tokens, etc.)"],"output_types":["text responses","streaming token streams","structured metadata (finish_reason, token_usage)"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_1","uri":"capability://text.generation.language.prompt.templating.with.variable.interpolation.and.message.composition","name":"prompt templating with variable interpolation and message composition","description":"Spring AI provides a Prompt abstraction that supports template-based message construction with variable substitution, role-based message lists (user/assistant/system), and fluent builder patterns. Templates use placeholder syntax (e.g., {variable}) that are resolved at runtime from a Map or Spring bean properties. The framework handles message ordering, role validation, and serialization to provider-specific formats (JSON for OpenAI, XML for Claude, etc.).","intents":["I want to define reusable prompt templates with variables that I can inject at runtime","I need to construct multi-turn conversations with proper role management (system/user/assistant)","I want to build dynamic prompts that adapt based on application state without string concatenation"],"best_for":["Applications with complex, multi-turn conversation flows","Teams building prompt libraries that need to be versioned and tested","Use cases requiring dynamic prompt composition based on user input or database queries"],"limitations":["Template syntax is basic (simple variable substitution) — no conditional logic or loops; complex logic requires Java code","No built-in prompt versioning or A/B testing framework","Message role validation is permissive; invalid role sequences aren't caught until provider API call"],"requires":["Java 17+","Spring Framework 6.0+","Understanding of chat message roles (system/user/assistant)"],"input_types":["template strings with {variable} placeholders","Map<String, Object> for variable values","Message objects with role and content"],"output_types":["Prompt objects containing ordered message lists","Provider-specific serialized formats (JSON/XML)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_10","uri":"capability://automation.workflow.retry.and.resilience.patterns.with.spring.retry.integration","name":"retry and resilience patterns with spring retry integration","description":"Spring AI integrates with Spring Retry to provide resilience for API calls to LLM providers. Developers can configure retry policies (exponential backoff, max attempts, retryable exceptions) via annotations (@Retryable) or programmatically. The framework retries on transient failures (rate limits, timeouts, temporary service unavailability) and fails fast on permanent errors (authentication, invalid input). Retry logic is transparent to application code; developers configure policies and Spring handles execution.","intents":["I want automatic retries for transient LLM API failures without writing retry logic","I need to handle rate limiting gracefully with exponential backoff","I want to distinguish between retryable and permanent failures"],"best_for":["Production applications requiring resilience to API failures","High-volume applications hitting rate limits","Teams wanting declarative retry policies without boilerplate"],"limitations":["Retry logic is request-level; no circuit breaker pattern for cascading failures","Exponential backoff can increase latency significantly for flaky services","No built-in retry budget or quota; unbounded retries can exhaust rate limits","Retries consume tokens/credits; cost implications not automatically tracked"],"requires":["Java 17+","Spring Framework 6.0+","Spring Retry on classpath","Configured retry policies"],"input_types":["API requests to LLM providers","Retry policies (max attempts, backoff strategy)"],"output_types":["Successful responses after retries","Failure after max attempts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_11","uri":"capability://automation.workflow.spring.boot.auto.configuration.and.property.based.provider.selection","name":"spring boot auto-configuration and property-based provider selection","description":"Spring AI provides Spring Boot auto-configuration that detects available LLM providers on the classpath and instantiates them based on application.yml properties. Developers declare dependencies (spring-ai-openai-spring-boot-starter, etc.) and configure properties (spring.ai.openai.api-key, spring.ai.openai.model-name); Spring Boot auto-configuration wires up ChatModel, EmbeddingModel, and VectorStore beans. No manual bean definitions required. Configuration properties support environment variable substitution and profiles, enabling different providers per environment (dev: Ollama, prod: OpenAI).","intents":["I want to configure my LLM provider via application.yml without writing bean definitions","I need different providers per environment (local Ollama, cloud OpenAI) without code changes","I want to inject ChatModel as a Spring bean without boilerplate"],"best_for":["Spring Boot applications wanting zero-configuration setup","Teams managing multiple environments with different providers","Developers preferring convention over configuration"],"limitations":["Auto-configuration is opinionated; advanced customization requires manual bean definitions","Property-based configuration doesn't support complex scenarios (multiple instances of same provider, custom client configuration)","No built-in validation of required properties; missing API keys cause runtime errors","Provider detection is classpath-based; unused providers on classpath can cause unexpected instantiation"],"requires":["Java 17+","Spring Boot 3.0+","Provider-specific starter (spring-ai-openai-spring-boot-starter, etc.)","application.yml with provider configuration"],"input_types":["application.yml properties","Environment variables","Spring profiles"],"output_types":["Instantiated ChatModel bean","Instantiated EmbeddingModel bean","Instantiated VectorStore bean"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_12","uri":"capability://automation.workflow.docker.compose.and.testcontainers.support.for.local.development","name":"docker compose and testcontainers support for local development","description":"Spring AI provides Docker Compose support for local development of vector stores and other services. Developers define docker-compose.yml with Chroma, Weaviate, or other services; Spring Boot auto-detects the compose file and starts containers automatically. Testcontainers integration enables integration tests that spin up ephemeral containers for each test. The framework handles container lifecycle management, port mapping, and connection details discovery via Spring Cloud Bindings.","intents":["I want to develop locally with the same vector store I'll use in production without manual container management","I need integration tests that spin up vector stores for each test run","I want Docker Compose configuration to be auto-detected and applied without manual setup"],"best_for":["Development teams wanting local-first RAG development","Integration testing of vector store operations","Teams using Docker Compose for local development infrastructure"],"limitations":["Docker Compose support is Spring Boot 3.1+; older versions require manual container management","Testcontainers add test startup time (~5-10 seconds per test class)","Container resource limits must be configured manually; no built-in optimization","Network isolation between containers requires careful configuration"],"requires":["Java 17+","Spring Boot 3.1+","Docker and Docker Compose installed","docker-compose.yml in project root"],"input_types":["docker-compose.yml configuration","Testcontainers annotations"],"output_types":["Running containers","Connection details via Spring Cloud Bindings","Ephemeral test containers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_13","uri":"capability://data.processing.analysis.embedding.model.abstraction.with.multi.provider.support","name":"embedding model abstraction with multi-provider support","description":"Spring AI provides an EmbeddingModel interface that abstracts embedding generation across providers (OpenAI, Azure, Ollama, Vertex AI, Bedrock). Developers call embed(text) or embed(List<String>) and receive embedding vectors; the framework handles provider-specific API calls, response normalization, and error handling. Like ChatModel, EmbeddingModel is configured via properties and auto-wired as a Spring bean. Embeddings are used for vector store ingestion and similarity search.","intents":["I want to generate embeddings without coupling to a specific provider","I need to switch embedding models (e.g., from OpenAI to Ollama) without code changes","I want to batch embed multiple texts efficiently"],"best_for":["RAG systems requiring flexible embedding model selection","Teams evaluating embedding models before production","Cost-sensitive applications wanting to switch from expensive to cheaper models"],"limitations":["Embedding dimensions vary by provider; vector store schema must accommodate different dimensions","No built-in embedding caching; repeated embeddings of same text are recomputed","Batch embedding API varies by provider; Spring AI normalizes to single interface but loses provider-specific optimizations","No built-in embedding quality metrics or evaluation"],"requires":["Java 17+","Spring Framework 6.0+","EmbeddingModel implementation for desired provider","Valid API credentials for embedding provider"],"input_types":["Text strings","List of texts for batch embedding"],"output_types":["Embedding vectors (float arrays)","Embedding metadata (model, dimensions)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_2","uri":"capability://planning.reasoning.advisors.framework.for.cross.cutting.ai.concerns.rag.memory.tool.calling","name":"advisors framework for cross-cutting ai concerns (rag, memory, tool-calling)","description":"Spring AI's Advisors framework provides a middleware pattern for injecting cross-cutting concerns into chat requests before they reach the model. Advisors intercept ChatClient calls, modify prompts (e.g., injecting retrieved documents), manage conversation memory, or augment requests with tool definitions. The framework uses a chain-of-responsibility pattern where multiple advisors can be composed; each advisor can read/modify the request and response. Built-in advisors include QuestionAnswerAdvisor (RAG), MessageChatMemoryAdvisor (conversation history), and ToolsAdvisor (function calling).","intents":["I want to automatically inject retrieved documents into prompts without modifying my chat logic","I need to maintain conversation history across multiple requests without manual state management","I want to augment certain requests with tool definitions while keeping others tool-free"],"best_for":["Teams building RAG systems that need clean separation between retrieval and chat logic","Multi-turn conversational applications requiring automatic memory management","Applications where different endpoints need different advisor configurations"],"limitations":["Advisor ordering matters but isn't explicitly validated; incorrect order can cause subtle bugs (e.g., memory advisor before RAG advisor)","No built-in advisor composition validation or conflict detection","Advisors add latency per request (retrieval, memory lookup, tool schema serialization); no caching layer for repeated queries","Limited to request/response interception — can't modify model behavior mid-stream"],"requires":["Java 17+","Spring Framework 6.0+","ChatClient (not raw ChatModel)","Vector store or memory implementation for RAG/memory advisors"],"input_types":["ChatClient requests","Prompt objects","Message lists"],"output_types":["Modified prompts with injected context","Augmented chat options with tool definitions","Chat responses with metadata"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_3","uri":"capability://tool.use.integration.multi.provider.function.calling.with.schema.based.tool.registration","name":"multi-provider function calling with schema-based tool registration","description":"Spring AI provides a schema-based function calling system that registers Java methods as tools, automatically generates JSON schemas from method signatures, and translates function calls across provider-specific formats (OpenAI's function_calling, Anthropic's tool_use, Vertex AI's function_calling). Developers annotate methods with @Tool and @ToolParam; Spring introspects the method signature to build schemas. When a model requests a function call, Spring matches the provider's response to the registered method, invokes it, and returns results back to the model for agentic loops.","intents":["I want to expose Java methods as tools to LLMs without manually writing JSON schemas","I need to support function calling across multiple LLM providers with a single tool definition","I want to build agentic loops where the model can call tools, receive results, and decide next steps"],"best_for":["Enterprise Java applications integrating LLMs with existing business logic","Teams building AI agents that need to interact with databases, APIs, or microservices","Multi-provider setups where tool definitions must work across OpenAI, Anthropic, and Vertex AI"],"limitations":["Schema generation from Java method signatures doesn't support complex nested types well; custom schema definitions required for deeply nested objects","No automatic type coercion — function parameters must match provider's JSON exactly; type mismatches cause runtime errors","Tool invocation is synchronous; long-running tools block the chat loop (no async/await pattern)","No built-in tool result validation or error handling — failed tool calls require manual retry logic"],"requires":["Java 17+","Spring Framework 6.0+","Methods must be Spring beans or components","Provider must support function calling (OpenAI, Anthropic, Vertex AI, Bedrock)"],"input_types":["Java methods with @Tool annotation","@ToolParam annotated parameters","Provider function call responses (JSON)"],"output_types":["JSON schemas for tool definitions","Tool invocation results (serialized to JSON)","Agentic loop responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_4","uri":"capability://memory.knowledge.vector.store.abstraction.with.pluggable.implementations","name":"vector store abstraction with pluggable implementations","description":"Spring AI provides a VectorStore interface that abstracts document storage and similarity search across 15+ vector database implementations (Chroma, Weaviate, Pinecone, Milvus, PostgreSQL pgvector, etc.). The framework handles document chunking, embedding generation, and vector persistence. Developers interact with a unified API (save(), similaritySearch(), delete()) regardless of underlying store. Spring Boot auto-configuration detects available vector stores and instantiates them; Docker Compose support enables local development with Testcontainers.","intents":["I want to build RAG systems without coupling to a specific vector database","I need to switch vector stores (e.g., from Chroma to Pinecone) without rewriting retrieval logic","I want local development with Docker Compose and production with managed services (Pinecone, Weaviate Cloud)"],"best_for":["Teams building RAG pipelines that want flexibility in vector store choice","Organizations evaluating multiple vector databases before production commitment","Development teams needing local-first development with Docker Compose"],"limitations":["VectorStore interface is lowest-common-denominator; advanced features (metadata filtering, hybrid search, reranking) require provider-specific implementations","No built-in document chunking strategy selection; default chunking may not suit all use cases (e.g., code requires different chunk sizes than prose)","Embedding generation is decoupled from storage; developers must manage embedding model consistency across stores","No built-in vector store health checks or failover; connection failures propagate to application"],"requires":["Java 17+","Spring Framework 6.0+","Spring Boot 3.0+ (for auto-configuration)","Running instance of chosen vector store (local Docker or managed service)","Embedding model configured (OpenAI, Ollama, etc.)"],"input_types":["Document objects (text content + metadata)","Query strings for similarity search","Embedding vectors (float arrays)"],"output_types":["List<Document> from similarity search","Similarity scores (float)","Document metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_5","uri":"capability://data.processing.analysis.etl.pipeline.for.document.processing.and.chunking","name":"etl pipeline for document processing and chunking","description":"Spring AI provides a DocumentReader abstraction and ETL pipeline for ingesting documents from multiple sources (PDF, text files, web pages, databases) and transforming them into chunks suitable for embedding and vector storage. The pipeline includes: DocumentReader (source abstraction), DocumentTransformer (chunking, filtering, metadata enrichment), and DocumentWriter (persistence to vector stores). Built-in readers support PDF, text, Markdown, and web content; chunking strategies include TokenTextSplitter (token-aware) and other configurable splitters. The framework handles encoding, metadata extraction, and batch processing.","intents":["I want to ingest PDFs and web content into a vector store without writing custom parsing code","I need to chunk documents intelligently (e.g., respecting code block boundaries) before embedding","I want to enrich documents with metadata (source URL, author, date) during ingestion"],"best_for":["Teams building RAG systems that need to ingest diverse document types","Applications requiring batch document processing and vector store population","Use cases where document metadata is critical for retrieval filtering"],"limitations":["PDF parsing is basic; complex layouts (tables, multi-column) may not parse correctly; requires custom DocumentReader for specialized formats","Chunking strategies are generic; code-aware chunking (respecting function boundaries) requires custom TokenTextSplitter implementation","No built-in deduplication; duplicate documents can be ingested multiple times","Batch processing is synchronous; large document sets block the application thread"],"requires":["Java 17+","Spring Framework 6.0+","DocumentReader implementation for desired source type","Vector store configured for DocumentWriter","Embedding model for vector generation"],"input_types":["PDF files","Text/Markdown files","Web URLs","Database queries","Custom document sources"],"output_types":["Document objects with chunked content","Metadata-enriched documents","Vectors persisted to vector store"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_6","uri":"capability://memory.knowledge.conversation.memory.management.with.pluggable.storage.backends","name":"conversation memory management with pluggable storage backends","description":"Spring AI provides a ChatMemory interface for storing and retrieving conversation history across requests, with pluggable backends (in-memory, database, Redis). The MessageChatMemoryAdvisor integrates memory into the chat flow: on each request, it retrieves prior messages, injects them into the prompt, and stores new messages after the response. Developers configure memory size (max messages), retention policy, and storage backend via properties. The framework handles message serialization, timestamp management, and conversation ID tracking.","intents":["I want multi-turn conversations where the model remembers prior exchanges without manual state management","I need to persist conversation history across application restarts or distributed instances","I want to limit memory size (e.g., last 10 messages) to control token usage and latency"],"best_for":["Conversational AI applications (chatbots, customer support)","Multi-user systems where each user has independent conversation history","Applications requiring conversation persistence for audit or analytics"],"limitations":["In-memory storage is lost on application restart; suitable only for development","No built-in conversation summarization; long conversations accumulate tokens and increase latency","Memory is per-conversation; no cross-conversation learning or semantic deduplication","No built-in privacy controls; sensitive information in conversation history isn't masked or encrypted"],"requires":["Java 17+","Spring Framework 6.0+","ChatMemory implementation (in-memory, database, or Redis)","ChatClient with MessageChatMemoryAdvisor configured"],"input_types":["Message objects (user/assistant/system)","Conversation IDs","Timestamps"],"output_types":["List<Message> from memory retrieval","Stored message records"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_7","uri":"capability://data.processing.analysis.structured.output.parsing.with.schema.validation","name":"structured output parsing with schema validation","description":"Spring AI provides output parsing that converts unstructured model responses into typed Java objects using JSON schema validation and deserialization. The framework supports multiple parsing strategies: BeanOutputParser (maps to Spring beans), JsonOutputParser (generic JSON), and provider-specific structured outputs (OpenAI's JSON mode, Anthropic's structured outputs). Developers define target classes with Jackson annotations; the parser generates JSON schemas, instructs the model to output JSON, and deserializes responses. Validation errors are caught and can trigger retries.","intents":["I want the model to return structured data (JSON) that I can deserialize into Java objects","I need to validate model outputs against a schema before using them in my application","I want to avoid parsing errors by instructing the model to output JSON and validating the result"],"best_for":["Applications extracting structured data from unstructured text (entity extraction, classification)","APIs returning model-generated JSON that must conform to a schema","Use cases where output validation is critical (e.g., database inserts)"],"limitations":["JSON mode is not guaranteed; models may still return non-JSON text; fallback parsing required","Schema generation from Java classes is basic; complex nested types or polymorphism require custom schemas","No built-in retry logic for parsing failures; applications must implement their own retry strategy","Structured output mode (OpenAI, Anthropic) may increase latency and token usage"],"requires":["Java 17+","Spring Framework 6.0+","Target class with Jackson annotations (@JsonProperty, etc.)","Provider supporting JSON mode or structured outputs"],"input_types":["Model responses (text or JSON)","Target Java classes","JSON schemas"],"output_types":["Typed Java objects","Validation errors","Parsed JSON"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_8","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.standardized.tool.communication","name":"model context protocol (mcp) integration for standardized tool communication","description":"Spring AI integrates with the Model Context Protocol (MCP), an open standard for LLM-to-tool communication developed by Anthropic. MCP provides a standardized way for models to discover, call, and receive results from tools via a client-server protocol. Spring AI's MCP support allows Java applications to act as MCP servers, exposing tools (functions, resources, prompts) to MCP-compatible clients. The framework handles MCP protocol serialization, tool discovery, and result marshaling, enabling interoperability with other MCP implementations.","intents":["I want to expose my Java application's tools to MCP-compatible clients (Claude, other LLMs)","I need standardized tool communication that works across multiple LLM providers","I want to build tool ecosystems where tools can be discovered and composed dynamically"],"best_for":["Organizations building tool ecosystems with multiple LLM providers","Teams integrating with Claude or other MCP-compatible models","Use cases requiring standardized tool discovery and composition"],"limitations":["MCP is relatively new; ecosystem maturity and tool availability are still developing","Spring AI's MCP support is newer than core features; may have edge cases or missing features","MCP adds protocol overhead; not suitable for latency-critical applications","Tool discovery and composition are standardized but not automated; applications must implement orchestration logic"],"requires":["Java 17+","Spring Framework 6.0+","MCP client (Claude, other compatible LLM)","Understanding of MCP protocol and tool definitions"],"input_types":["Tool definitions (functions, resources, prompts)","MCP protocol messages","Tool invocation requests"],"output_types":["MCP protocol responses","Tool results","Tool discovery metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__cap_9","uri":"capability://automation.workflow.observability.and.monitoring.with.spring.boot.actuator.integration","name":"observability and monitoring with spring boot actuator integration","description":"Spring AI integrates with Spring Boot Actuator to provide observability for AI operations: token usage metrics (input/output tokens per model), latency measurements, error rates, and custom metrics. The framework uses Micrometer for metrics collection and Spring Cloud Sleuth for distributed tracing. Developers can monitor token consumption per model, track API costs, identify slow operations, and correlate AI requests with application traces. Metrics are exposed via Actuator endpoints (/actuator/metrics) and can be exported to monitoring systems (Prometheus, Datadog, etc.).","intents":["I want to track token usage and API costs per model and endpoint","I need to identify performance bottlenecks in my AI pipeline (retrieval, model latency, etc.)","I want to correlate AI requests with application traces for debugging"],"best_for":["Production AI applications requiring cost tracking and optimization","Teams monitoring distributed AI systems with multiple services","Applications needing compliance/audit trails for AI operations"],"limitations":["Metrics are basic; no built-in cost calculation (requires custom logic to multiply tokens by pricing)","Tracing requires Spring Cloud Sleuth configuration; not automatic","No built-in alerting; requires external monitoring system (Prometheus, Datadog) for alerts","Metrics granularity is limited; no per-request breakdown of retrieval vs. model latency"],"requires":["Java 17+","Spring Framework 6.0+","Spring Boot 3.0+ with Actuator","Micrometer for metrics export","Optional: Spring Cloud Sleuth for tracing"],"input_types":["AI operations (chat, embeddings, etc.)","Token counts from provider responses"],"output_types":["Metrics (token counts, latency, error rates)","Distributed traces","Actuator endpoints (/actuator/metrics)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spring-ai__headline","uri":"capability://data.processing.analysis.ai.framework.for.spring.ecosystem","name":"ai framework for spring ecosystem","description":"Spring AI is a comprehensive framework designed for building AI-powered applications within the Spring ecosystem, offering seamless integration with various AI model providers and advanced features like RAG and ETL pipelines.","intents":["best AI framework for Spring","AI framework for enterprise Java","Spring AI for RAG applications","Spring AI integration with OpenAI","AI development framework for Java/Kotlin"],"best_for":["enterprise Java applications","developers using Spring Boot"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Java 17+","Spring Framework 6.0+","Spring Boot 3.0+ (for auto-configuration)","Valid API credentials for at least one supported provider","Understanding of chat message roles (system/user/assistant)","Spring Retry on classpath","Configured retry policies","Spring Boot 3.0+","Provider-specific starter (spring-ai-openai-spring-boot-starter, etc.)","application.yml with provider configuration"],"failure_modes":["Provider-specific features (e.g., OpenAI's vision_detail parameter) require custom ChatOptions subclasses, breaking abstraction","Response streaming behavior varies subtly between providers; normalization adds ~50-100ms latency","No automatic fallback or load-balancing across providers — requires external orchestration","Template syntax is basic (simple variable substitution) — no conditional logic or loops; complex logic requires Java code","No built-in prompt versioning or A/B testing framework","Message role validation is permissive; invalid role sequences aren't caught until provider API call","Retry logic is request-level; no circuit breaker pattern for cascading failures","Exponential backoff can increase latency significantly for flaky services","No built-in retry budget or quota; unbounded retries can exhaust rate limits","Retries consume tokens/credits; cost implications not automatically tracked","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.6,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=spring-ai","compare_url":"https://unfragile.ai/compare?artifact=spring-ai"}},"signature":"wi75tfo5IZMDXRK2n1dhSiBFdFW6D51c5eKhmouvEAO7Qlcz5I5sWGl4gsN+ww07uQwzkdfmbhkXRi11Q+xAAg==","signedAt":"2026-06-22T16:38:18.434Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/spring-ai","artifact":"https://unfragile.ai/spring-ai","verify":"https://unfragile.ai/api/v1/verify?slug=spring-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}