{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langchain4j--langchain4j","slug":"langchain4j--langchain4j","name":"langchain4j","type":"framework","url":"https://docs.langchain4j.dev","page_url":"https://unfragile.ai/langchain4j--langchain4j","categories":["frameworks-sdks","rag-knowledge"],"tags":["anthropic","chatgpt","chroma","embeddings","gemini","gpt","huggingface","java","langchain","llama","llm","llms","milvus","ollama","onnx","openai","openai-api","pgvector","pinecone","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langchain4j--langchain4j__cap_0","uri":"capability://tool.use.integration.unified.multi.provider.llm.abstraction.with.provider.agnostic.interfaces","name":"unified multi-provider llm abstraction with provider-agnostic interfaces","description":"LangChain4j defines common interfaces (ChatLanguageModel, StreamingChatLanguageModel, LanguageModel) that abstract over 25+ LLM provider implementations including OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Azure OpenAI. Developers write application code once against these interfaces and swap providers via dependency injection or configuration without code changes. The framework handles provider-specific API translation, authentication, and response normalization internally.","intents":["switch between LLM providers without rewriting application code","avoid vendor lock-in by maintaining provider-agnostic business logic","test with local models (Ollama, LocalAI) in development and cloud providers in production","compare model performance across providers with identical application code"],"best_for":["enterprise Java teams building LLM applications requiring provider flexibility","developers migrating between cloud LLM providers","teams wanting to avoid proprietary API coupling"],"limitations":["abstraction may hide provider-specific capabilities (e.g., vision features, function calling variants) requiring fallback to provider-specific APIs","response normalization adds ~50-100ms overhead per request due to translation layer","not all providers support identical feature sets — some advanced features require provider-specific code paths"],"requires":["Java 11+","Maven or Gradle for dependency management","API credentials for at least one LLM provider (OpenAI, Anthropic, Google, AWS, Azure, etc.)"],"input_types":["text prompts","chat message sequences","structured system/user/assistant messages"],"output_types":["text responses","structured JSON via schema validation","streaming token sequences"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_1","uri":"capability://tool.use.integration.declarative.ai.services.with.annotation.driven.interface.generation","name":"declarative ai services with annotation-driven interface generation","description":"LangChain4j's AI Services framework uses Java annotations (@AiService, @SystemMessage, @UserMessage, @ToolCall) to declaratively define LLM-powered service interfaces. The framework generates proxy implementations at runtime that handle prompt templating, message construction, tool invocation, and response parsing. This pattern eliminates boilerplate for common LLM interaction patterns and integrates seamlessly with Spring/Quarkus dependency injection.","intents":["define LLM-powered services with minimal boilerplate using annotations","automatically handle prompt templating and message construction","integrate LLM services into existing Spring Boot/Quarkus applications via @Bean injection","build type-safe conversational interfaces with compile-time validation"],"best_for":["Spring Boot and Quarkus developers building LLM-powered microservices","teams wanting rapid prototyping of chatbot/assistant services","developers preferring declarative configuration over imperative API calls"],"limitations":["annotation-driven approach requires understanding of Spring/Quarkus proxy mechanics — debugging can be opaque","complex multi-turn conversations with dynamic tool selection may require custom implementation beyond annotation capabilities","proxy generation adds ~200-500ms startup overhead per service class"],"requires":["Java 11+","Spring Boot 2.7+ or Quarkus 2.0+","Maven/Gradle with annotation processing enabled","LLM provider API key and ChatLanguageModel bean configured"],"input_types":["method parameters (automatically templated into prompts)","Java objects (serialized to JSON for LLM context)"],"output_types":["method return types (String, custom POJOs, streaming Response<T>)","structured responses parsed from LLM output"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_10","uri":"capability://automation.workflow.observability.and.metrics.collection.with.structured.logging.and.tracing","name":"observability and metrics collection with structured logging and tracing","description":"LangChain4j integrates observability through structured logging of LLM calls, tool invocations, and agent steps. The framework provides hooks for metrics collection (token counts, latency, cost) and integrates with common observability platforms. Logging captures request/response details, token usage, and execution traces for debugging and monitoring. Integration with Spring Boot actuators enables production monitoring.","intents":["monitor LLM application performance and costs in production","debug LLM behavior through detailed execution traces","track token usage and costs across LLM calls","integrate with observability platforms (Prometheus, DataDog, etc.)"],"best_for":["production LLM applications requiring cost and performance monitoring","teams debugging complex agent or RAG behavior","applications requiring compliance audit trails"],"limitations":["structured logging adds overhead to every LLM call — may impact latency-sensitive applications","token counting is approximate — actual costs may differ from estimates","observability integration is framework-specific — requires custom adapters for non-standard platforms"],"requires":["Java 11+","SLF4J or similar logging framework","Optional: Spring Boot actuators for metrics exposure"],"input_types":["LLM calls, tool invocations, agent steps"],"output_types":["structured logs (JSON format)","metrics (token counts, latency, costs)","execution traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_11","uri":"capability://tool.use.integration.skills.system.for.modular.reusable.llm.powered.capabilities","name":"skills system for modular, reusable llm-powered capabilities","description":"LangChain4j provides a Skills system that packages LLM-powered capabilities (e.g., summarization, translation, classification) as reusable, composable modules. Skills are defined as interfaces with @Skill annotations and can be combined to build complex applications. The framework handles skill invocation, parameter passing, and result composition, allowing skills to be shared across applications and teams.","intents":["package LLM-powered capabilities as reusable skills","compose multiple skills to build complex applications","share skills across teams and applications","build skill libraries for common LLM patterns"],"best_for":["teams building reusable LLM-powered libraries","organizations standardizing on common LLM capabilities","applications requiring composition of multiple LLM tasks"],"limitations":["skill composition adds complexity to application architecture","no built-in skill discovery or marketplace — requires manual skill registration","skill parameter passing may require serialization/deserialization overhead"],"requires":["Java 11+","Skill interface definitions with @Skill annotations"],"input_types":["skill parameters (text, objects)"],"output_types":["skill results (text, objects)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_12","uri":"capability://data.processing.analysis.embedding.model.abstraction.with.multiple.provider.support.and.local.model.options","name":"embedding model abstraction with multiple provider support and local model options","description":"LangChain4j provides EmbeddingModel interface with implementations for OpenAI, Ollama, HuggingFace, Google Gemini, Anthropic, and other providers. The framework handles embedding generation, caching, and batch processing. Support for local models (Ollama, ONNX) enables privacy-preserving embeddings without cloud dependencies. Embeddings are used for RAG, semantic search, and similarity comparisons.","intents":["generate embeddings for documents and queries using multiple providers","switch between cloud and local embedding models","implement semantic search and similarity comparisons","build RAG systems with flexible embedding model selection"],"best_for":["RAG applications requiring flexible embedding model selection","teams wanting privacy-preserving embeddings via local models","applications comparing embedding quality across providers"],"limitations":["embedding quality varies significantly across models — no universal best model","local models (Ollama, ONNX) require GPU for reasonable performance","embedding caching is not built-in — requires external caching layer for cost optimization"],"requires":["Java 11+","EmbeddingModel implementation (OpenAI, Ollama, HuggingFace, Google, Anthropic, etc.)","Optional: GPU for local embedding models"],"input_types":["text documents","queries"],"output_types":["embedding vectors (float arrays)","embedding metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_13","uri":"capability://data.processing.analysis.document.loading.and.chunking.with.multiple.format.support.and.configurable.splitting.strategies","name":"document loading and chunking with multiple format support and configurable splitting strategies","description":"LangChain4j provides DocumentLoader interface with implementations for PDF, HTML, Markdown, and classpath resources. The framework includes DocumentSplitter strategies (recursive character splitting, token-based splitting, semantic splitting) for chunking documents into retrieval-friendly segments. Loaders handle format-specific parsing and metadata extraction. Chunking strategies are configurable to balance retrieval granularity and context window usage.","intents":["load documents from multiple formats (PDF, HTML, Markdown, text)","chunk documents into retrieval-friendly segments","extract metadata from documents for filtering and ranking","implement RAG document pipelines"],"best_for":["RAG applications requiring document ingestion and processing","teams building knowledge bases from diverse document formats","applications needing flexible document chunking strategies"],"limitations":["PDF parsing is complex — scanned PDFs or complex layouts may fail","chunking strategy significantly affects retrieval quality — no one-size-fits-all approach","metadata extraction is format-specific — requires custom implementation for custom formats"],"requires":["Java 11+","DocumentLoader implementation for target format","DocumentSplitter implementation for chunking strategy"],"input_types":["documents (PDF, HTML, Markdown, text files, classpath resources)"],"output_types":["Document objects with content and metadata","chunked document segments"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_14","uri":"capability://tool.use.integration.spring.boot.and.quarkus.framework.integration.with.automatic.bean.configuration","name":"spring boot and quarkus framework integration with automatic bean configuration","description":"LangChain4j provides Spring Boot and Quarkus integration modules that automatically configure LLM providers, embedding stores, and AI Services as Spring/Quarkus beans. The framework uses @ConditionalOnProperty and @ConditionalOnClass to enable providers based on classpath and configuration. AI Services are automatically registered as beans and can be injected into application code. Configuration is externalized via application.properties/application.yml.","intents":["integrate LLM capabilities into Spring Boot applications with minimal boilerplate","configure LLM providers via application.properties without code changes","inject AI Services into Spring components via @Autowired","build Quarkus native images with LLM support"],"best_for":["Spring Boot and Quarkus developers building LLM applications","teams wanting framework-native LLM integration","applications requiring externalized LLM configuration"],"limitations":["Spring/Quarkus integration adds startup overhead — may impact cold start latency","automatic bean configuration can be opaque — debugging requires understanding Spring/Quarkus mechanics","Quarkus native image support requires GraalVM configuration — not all providers are GraalVM-compatible"],"requires":["Spring Boot 2.7+ or Quarkus 2.0+","langchain4j-spring-boot-starter or langchain4j-quarkus module","LLM provider API key in application.properties"],"input_types":["application.properties/application.yml configuration"],"output_types":["Spring/Quarkus beans (ChatLanguageModel, EmbeddingModel, AI Services)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_2","uri":"capability://tool.use.integration.schema.based.function.calling.with.multi.provider.tool.binding","name":"schema-based function calling with multi-provider tool binding","description":"LangChain4j implements tool calling through a schema-based function registry that generates provider-specific function schemas (OpenAI, Anthropic, Google, etc.) from Java method signatures and annotations. The framework handles tool invocation routing, parameter marshalling, and result injection back into the conversation context. It supports both explicit tool definition via @Tool annotations and automatic schema generation from method signatures.","intents":["enable LLMs to invoke Java methods as tools with automatic schema generation","support multi-provider function calling with provider-specific schema adaptation","handle tool result injection and conversation continuation after tool execution","build agents that can dynamically select and invoke tools based on task requirements"],"best_for":["developers building LLM agents that need to invoke Java services/APIs","teams implementing tool-use patterns across multiple LLM providers","applications requiring deterministic tool execution with LLM orchestration"],"limitations":["schema generation from Java signatures may not capture all semantic constraints — complex validation logic requires manual schema refinement","tool execution errors are passed back to LLM for recovery — no built-in retry logic or circuit breakers","provider-specific tool calling semantics (parallel tool calls, tool choice enforcement) require provider-specific configuration"],"requires":["Java 11+","LLM provider supporting function calling (OpenAI, Anthropic, Google, Bedrock, Azure)","Tool methods must be public and have serializable parameters/return types"],"input_types":["Java method signatures with @Tool annotations","method parameters (automatically serialized to JSON schema)","LLM-generated tool invocation requests"],"output_types":["provider-specific function schemas (OpenAI functions, Anthropic tools, Google function declarations)","tool execution results injected as assistant messages","structured tool call responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.standardized.tool.composition","name":"model context protocol (mcp) integration for standardized tool composition","description":"LangChain4j integrates the Model Context Protocol (MCP) standard, allowing applications to compose tools from MCP servers (both local and remote) alongside native Java tools. The framework handles MCP client initialization, server communication, tool discovery, and schema translation from MCP tool definitions into provider-specific function calling formats. This enables interoperability with ecosystem tools (file systems, web search, databases) without custom integration code.","intents":["compose tools from MCP servers (file systems, web search, databases) with native Java tools","build agents that can discover and invoke tools from multiple MCP servers","enable standardized tool composition across different LLM applications","avoid custom integration code for common tool patterns by leveraging MCP ecosystem"],"best_for":["developers building agents requiring access to external tools (file systems, web APIs, databases)","teams adopting MCP standard for tool composition","applications needing to compose tools from multiple sources (native Java + MCP servers)"],"limitations":["MCP server availability and latency directly impact agent performance — no built-in caching or fallback mechanisms","schema translation between MCP and provider-specific formats may lose provider-specific capabilities (e.g., tool choice enforcement)","debugging tool invocation chains across MCP boundaries requires tracing across process boundaries"],"requires":["Java 11+","MCP server running (local or remote) with stdio or HTTP transport","LLM provider supporting function calling","langchain4j-mcp module in classpath"],"input_types":["MCP server URIs (stdio or HTTP)","MCP tool definitions (auto-discovered from server)","LLM-generated tool invocation requests"],"output_types":["MCP tool execution results","translated function schemas for LLM providers","structured tool responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_4","uri":"capability://text.generation.language.streaming.response.handling.with.backpressure.and.token.level.control","name":"streaming response handling with backpressure and token-level control","description":"LangChain4j provides StreamingChatLanguageModel and streaming response handlers that emit tokens as they arrive from the LLM provider, enabling real-time streaming UI updates and token-level processing. The framework implements backpressure mechanisms to prevent buffer overflow when consuming tokens slower than the LLM produces them, and supports token counting via TokenCountEstimator for cost tracking and context window management.","intents":["stream LLM responses token-by-token to UI for real-time user feedback","process tokens as they arrive without buffering entire responses in memory","implement backpressure to handle slow consumers without memory exhaustion","track token consumption for cost estimation and context window management"],"best_for":["web applications requiring real-time streaming responses (chat UIs, code generation)","applications with memory constraints needing token-level streaming","teams building cost-aware LLM applications requiring token counting"],"limitations":["streaming responses cannot be retried mid-stream — partial responses are lost on connection failure","token counting is approximate (uses provider-specific tokenizers) and may not match actual billing","backpressure handling adds complexity to error recovery — partial results may be inconsistent"],"requires":["Java 11+","LLM provider supporting streaming (OpenAI, Anthropic, Google, Bedrock, Azure)","StreamingChatLanguageModel implementation for chosen provider"],"input_types":["chat messages","prompts"],"output_types":["streaming token sequences (via StreamingResponseHandler callbacks)","token count estimates","partial response accumulation"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_5","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation.and.type.safe.parsing","name":"structured output generation with json schema validation and type-safe parsing","description":"LangChain4j enables structured output generation by accepting JSON schemas or Java POJO types, generating appropriate prompts/schemas for the LLM, and parsing responses back into strongly-typed Java objects. The framework uses provider-specific structured output APIs (OpenAI's JSON mode, Anthropic's tool use, Google's JSON schema) and falls back to prompt-based generation for providers without native support. Response validation ensures schema compliance before returning to application code.","intents":["generate structured data (JSON, objects) from LLM with type-safe parsing","validate LLM responses against JSON schemas before application use","extract entities, relationships, or structured information from unstructured text","build data pipelines that consume LLM-generated structured data"],"best_for":["applications extracting structured data from LLM responses (entity extraction, data classification)","data pipelines consuming LLM-generated JSON or objects","teams requiring type-safe LLM integration with compile-time validation"],"limitations":["LLMs may violate JSON schemas despite constraints — validation failures require retry logic or fallback parsing","schema complexity affects LLM performance — deeply nested schemas may cause hallucination or truncation","provider-specific structured output APIs have different reliability — fallback to prompt-based generation may be less reliable"],"requires":["Java 11+","LLM provider supporting structured output (OpenAI with gpt-4-turbo+, Anthropic, Google, Bedrock)","POJO classes or JSON schema definitions for target structure"],"input_types":["Java POJO types (via reflection)","JSON schema definitions","unstructured text prompts"],"output_types":["strongly-typed Java objects","validated JSON structures","parsed and deserialized POJOs"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_6","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.pluggable.embedding.stores.and.document.processing","name":"retrieval-augmented generation (rag) with pluggable embedding stores and document processing","description":"LangChain4j implements RAG through a modular architecture: DocumentSplitter for chunking documents, EmbeddingModel for generating embeddings, and EmbeddingStore interface with 10+ implementations (Pinecone, Milvus, Weaviate, Chroma, PostgreSQL/pgvector, Cassandra, Elasticsearch, etc.). The framework provides ContentRetriever for semantic search and automatic context injection into prompts. Document loaders handle multiple formats (PDF, HTML, Markdown, classpath resources).","intents":["build RAG applications that augment LLM context with retrieved documents","implement semantic search over document collections using embeddings","switch between embedding stores without application code changes","process and chunk documents for efficient retrieval"],"best_for":["teams building knowledge-base chatbots or Q&A systems","applications requiring semantic search over large document collections","enterprises needing to keep LLM context grounded in proprietary documents"],"limitations":["embedding quality directly impacts retrieval quality — poor embeddings lead to irrelevant context injection","chunking strategy significantly affects retrieval performance — no one-size-fits-all approach","embedding store latency adds 100-500ms per retrieval — not suitable for sub-100ms response requirements","no built-in reranking or relevance filtering — all retrieved documents are injected into context"],"requires":["Java 11+","EmbeddingModel implementation (OpenAI, Ollama, HuggingFace, etc.)","EmbeddingStore implementation (Pinecone, Milvus, Weaviate, Chroma, PostgreSQL, etc.)","Documents in supported formats (PDF, HTML, Markdown, plain text)"],"input_types":["documents (PDF, HTML, Markdown, text files)","user queries (text)","document chunks"],"output_types":["document embeddings (vector representations)","retrieved document chunks","augmented prompts with context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_7","uri":"capability://memory.knowledge.chat.memory.and.conversation.context.management.with.multiple.storage.backends","name":"chat memory and conversation context management with multiple storage backends","description":"LangChain4j provides ChatMemory interface with multiple implementations (in-memory, persistent) for managing conversation history. The framework handles message storage, retrieval, and context window management. Integration with Spring/Quarkus allows automatic memory injection into AI Services. Memory can be scoped per conversation, per user, or globally, with configurable retention policies and message summarization for long conversations.","intents":["maintain conversation history across multiple turns in chatbot applications","manage context window by summarizing or pruning old messages","persist conversations for audit, replay, or analytics","scope memory per user or conversation in multi-user applications"],"best_for":["chatbot and conversational AI applications requiring multi-turn context","applications needing conversation persistence for compliance or analytics","teams building user-scoped conversation management"],"limitations":["in-memory implementations lose conversations on application restart — requires external persistence","message summarization for context window management may lose important details","no built-in message deduplication or conflict resolution for concurrent updates","memory scoping (per-user, per-conversation) requires manual management in multi-tenant applications"],"requires":["Java 11+","ChatMemory implementation (in-memory or persistent)","Optional: database for persistent storage (Spring Data JPA, etc.)"],"input_types":["chat messages (user, assistant, system)","conversation identifiers"],"output_types":["message history (list of ChatMessage objects)","summarized context for context window management"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_8","uri":"capability://planning.reasoning.agentic.systems.with.loop.orchestration.and.tool.use.planning","name":"agentic systems with loop orchestration and tool-use planning","description":"LangChain4j implements agent patterns through an Agent interface that orchestrates the reasoning loop: LLM reasoning → tool selection → tool execution → result injection → repeat until termination. The framework provides ReActAgent (Reasoning + Acting) and other agent implementations with configurable termination conditions, max iterations, and error handling. Agents integrate with the tool calling system for automatic tool invocation and result processing.","intents":["build autonomous agents that reason about tasks and invoke tools to accomplish goals","implement ReAct (Reasoning + Acting) pattern for complex multi-step tasks","orchestrate tool selection and execution based on LLM reasoning","handle agent termination conditions and error recovery"],"best_for":["applications requiring autonomous task execution (research agents, automation workflows)","teams building complex multi-step reasoning systems","developers implementing ReAct or similar agentic patterns"],"limitations":["agent loops can be expensive — each iteration requires LLM inference, tool execution, and context management","no built-in cost tracking or budget enforcement — agents can exceed cost limits without control","error recovery is limited — tool failures may cause agent loops to diverge or get stuck","debugging agent behavior is complex — requires tracing across multiple LLM calls and tool invocations"],"requires":["Java 11+","ChatLanguageModel implementation","Tool definitions (via @Tool or function calling)","Termination condition strategy (max iterations, goal achievement, etc.)"],"input_types":["user goals/tasks (text)","available tools (function definitions)"],"output_types":["agent execution trace (reasoning steps, tool calls, results)","final agent output (text, structured data)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain4j--langchain4j__cap_9","uri":"capability://safety.moderation.guardrails.and.content.moderation.with.pluggable.validators.and.filters","name":"guardrails and content moderation with pluggable validators and filters","description":"LangChain4j provides guardrails through OutputParser and validator patterns that can filter, validate, or transform LLM outputs before returning to application code. The framework supports content moderation via integration with moderation APIs (OpenAI Moderation, custom validators) and allows chaining multiple validators. Guardrails can enforce schema compliance, filter harmful content, or validate business logic constraints.","intents":["filter harmful or inappropriate content from LLM responses","validate LLM outputs against business logic constraints","enforce schema compliance and data quality","implement content moderation policies in LLM applications"],"best_for":["applications requiring content moderation (public-facing chatbots, user-generated content)","teams enforcing business logic constraints on LLM outputs","compliance-sensitive applications (healthcare, finance)"],"limitations":["guardrails add latency to response processing — moderation APIs may add 100-500ms per response","no built-in retry logic for failed validation — applications must implement recovery","guardrails are applied post-generation — cannot prevent harmful content generation, only filter results","custom validators require manual implementation — no built-in library of common constraints"],"requires":["Java 11+","OutputParser or custom validator implementation","Optional: moderation API (OpenAI Moderation, etc.)"],"input_types":["LLM responses (text, structured data)"],"output_types":["validated/filtered responses","moderation scores or flags"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Java 11+","Maven or Gradle for dependency management","API credentials for at least one LLM provider (OpenAI, Anthropic, Google, AWS, Azure, etc.)","Spring Boot 2.7+ or Quarkus 2.0+","Maven/Gradle with annotation processing enabled","LLM provider API key and ChatLanguageModel bean configured","SLF4J or similar logging framework","Optional: Spring Boot actuators for metrics exposure","Skill interface definitions with @Skill annotations","EmbeddingModel implementation (OpenAI, Ollama, HuggingFace, Google, Anthropic, etc.)"],"failure_modes":["abstraction may hide provider-specific capabilities (e.g., vision features, function calling variants) requiring fallback to provider-specific APIs","response normalization adds ~50-100ms overhead per request due to translation layer","not all providers support identical feature sets — some advanced features require provider-specific code paths","annotation-driven approach requires understanding of Spring/Quarkus proxy mechanics — debugging can be opaque","complex multi-turn conversations with dynamic tool selection may require custom implementation beyond annotation capabilities","proxy generation adds ~200-500ms startup overhead per service class","structured logging adds overhead to every LLM call — may impact latency-sensitive applications","token counting is approximate — actual costs may differ from estimates","observability integration is framework-specific — requires custom adapters for non-standard platforms","skill composition adds complexity to application architecture","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6982353319003737,"quality":0.6,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-01T21:32:36Z"},"community":{"stars":11841,"forks":2185,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain4j--langchain4j","compare_url":"https://unfragile.ai/compare?artifact=langchain4j--langchain4j"}},"signature":"f+yQFDOWH0c41eLewD4s1i0zT0LHl7cLOZXBT4LzKeMkMxhf1uNPzI0HCqbUtT7T9H5v4QXJ08uqW65YSgJoCQ==","signedAt":"2026-06-20T03:04:31.606Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain4j--langchain4j","artifact":"https://unfragile.ai/langchain4j--langchain4j","verify":"https://unfragile.ai/api/v1/verify?slug=langchain4j--langchain4j","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"}}