{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"langchain-templates","slug":"langchain-templates","name":"LangChain Templates","type":"template","url":"https://github.com/langchain-ai/langchain/tree/master/templates","page_url":"https://unfragile.ai/langchain-templates","categories":["app-builders","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"langchain-templates__cap_0","uri":"capability://memory.knowledge.langserve.based.rag.template.deployment.with.vector.store.abstraction","name":"langserve-based rag template deployment with vector store abstraction","description":"Provides pre-built Retrieval-Augmented Generation templates that abstract over multiple vector store backends (Pinecone, Weaviate, Chroma, FAISS) through LangChain's Runnable interface, enabling developers to swap vector stores without changing application code. Templates use LCEL (LangChain Expression Language) to compose retriever chains with LLM calls, handling document ingestion, embedding generation, and semantic search orchestration as a single deployable LangServe application.","intents":["Deploy a production RAG system without writing vector store integration code","Switch between vector store providers (Pinecone to Weaviate) by changing configuration","Build a semantic search pipeline that chains embeddings, retrieval, and LLM generation","Serve RAG chains via REST API with automatic OpenAPI schema generation"],"best_for":["Teams building document Q&A systems with pluggable vector stores","Developers migrating between vector store providers","Rapid prototyping of RAG applications with minimal infrastructure setup"],"limitations":["Vector store selection is compile-time configuration — runtime switching requires application restart","No built-in multi-tenancy or document isolation — requires external access control layer","Embedding model selection is fixed per template — no dynamic model switching","Templates assume synchronous retrieval — no streaming retrieval support out-of-box"],"requires":["Python 3.10+","LangChain 1.2.6+","LangServe for REST API deployment","API keys for chosen LLM provider (OpenAI, Anthropic, etc.)","Vector store credentials (Pinecone, Weaviate, or local FAISS setup)"],"input_types":["documents (PDF, text, markdown)","user queries (text)","vector store configuration (JSON/YAML)"],"output_types":["retrieved documents with relevance scores","LLM-generated answers with source citations","REST API responses (JSON)"],"categories":["memory-knowledge","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_1","uri":"capability://data.processing.analysis.extraction.chain.templates.with.structured.output.schema.binding","name":"extraction chain templates with structured output schema binding","description":"Provides templates for extracting structured data from unstructured text using LLMs with Pydantic schema binding, enabling type-safe extraction without manual prompt engineering. Templates use LangChain's structured output patterns (via tool calling or JSON mode) to guarantee schema compliance, with built-in retry logic via Tenacity for handling LLM parsing failures and automatic validation against the defined schema.","intents":["Extract entities, relationships, or facts from documents with guaranteed schema compliance","Convert unstructured text into structured JSON matching a Pydantic model","Build data pipelines that validate LLM outputs before downstream processing","Handle extraction failures gracefully with automatic retry and fallback strategies"],"best_for":["Data engineering teams building LLM-powered ETL pipelines","Developers extracting structured data from documents for database ingestion","Applications requiring guaranteed output schema validation"],"limitations":["Schema complexity is limited by LLM context window and token budget — deeply nested schemas may exceed practical limits","Extraction accuracy depends on LLM capability — smaller models (Llama 2) perform worse than GPT-4 on complex schemas","Retry logic adds latency — failed extractions may require 2-3 LLM calls before succeeding","No built-in handling of partial extraction — if one field fails validation, entire extraction fails"],"requires":["Python 3.10+","LangChain 1.2.6+","Pydantic 2.7.4+ for schema definition","LLM with function calling or JSON mode support (OpenAI, Anthropic, Groq)"],"input_types":["unstructured text","documents (PDF, HTML, markdown)","Pydantic schema definitions"],"output_types":["validated Pydantic model instances","structured JSON matching schema","extraction metadata (confidence, retry count)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_10","uri":"capability://tool.use.integration.tool.and.function.calling.abstraction.with.schema.based.invocation","name":"tool and function calling abstraction with schema-based invocation","description":"Templates use LangChain's tool abstraction to expose functions as callable tools that LLMs can invoke through function calling APIs (OpenAI, Anthropic) or tool-use protocols. Tools are defined with Pydantic schemas that describe inputs and outputs, enabling LLMs to generate properly-typed function calls without manual parsing. The tool abstraction handles schema serialization, argument validation, and error handling, with support for both synchronous and asynchronous tool execution.","intents":["Enable LLMs to call external functions (APIs, databases, custom code) through function calling","Define tools with strict input schemas to ensure LLM-generated calls are valid","Build agents that iteratively call tools based on task requirements","Handle tool execution errors gracefully and allow LLMs to retry with different arguments"],"best_for":["Developers building LLM agents that need to interact with external systems","Teams implementing tool-use patterns (agents, ReAct)","Applications requiring structured function calling with schema validation"],"limitations":["LLM function calling quality varies — some models (Llama 2) struggle with complex schemas","Schema size is limited by context window — tools with many parameters may exceed token budget","Tool execution is synchronous by default — async tools require explicit configuration","Error handling is manual — LLM doesn't automatically retry on tool execution errors","Tool discovery is manual — no automatic tool recommendation based on task"],"requires":["Python 3.10+","LangChain 1.2.6+","Pydantic 2.7.4+ for schema definition","LLM with function calling support (OpenAI, Anthropic, Groq)"],"input_types":["Python functions with type annotations","Pydantic schema definitions","tool configuration (name, description)"],"output_types":["tool invocations (function name + arguments)","tool execution results","error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_11","uri":"capability://planning.reasoning.agent.framework.integration.with.middleware.and.tool.routing","name":"agent framework integration with middleware and tool routing","description":"Templates integrate LangChain's agent system (built on LangGraph) to enable autonomous agents that iteratively plan, invoke tools, and refine strategies based on results. Agents use middleware patterns to intercept and modify tool calls, implement custom routing logic to select appropriate tools, and support both ReAct (reasoning + acting) and other agentic patterns. The agent framework handles tool loop orchestration, error recovery, and state management, with built-in support for streaming agent steps.","intents":["Build autonomous agents that plan and execute multi-step tasks without explicit orchestration","Implement custom tool routing logic to select appropriate tools based on task context","Handle agent errors and implement recovery strategies (retry, fallback tools)","Stream agent steps for real-time visibility into agent reasoning and actions"],"best_for":["Teams building autonomous agents for complex tasks","Developers implementing ReAct or other agentic patterns","Applications requiring multi-step planning and execution"],"limitations":["Agent planning is unreliable — agents may get stuck in loops or choose suboptimal tools","Token usage is high — agents make multiple LLM calls per task, increasing costs","Debugging agent behavior is difficult — reasoning traces are hard to interpret","Tool selection is not optimized — agents may invoke wrong tools or miss relevant tools","State management is manual — agents don't automatically persist state across sessions"],"requires":["Python 3.10+","LangChain 1.2.6+","LangGraph 1.0.2+ for agent orchestration","LLM with function calling support"],"input_types":["task description (text)","available tools (list of Tool objects)","agent configuration (model, temperature, max iterations)"],"output_types":["agent steps (thought, action, observation)","final result","execution trace"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_12","uri":"capability://automation.workflow.testing.and.validation.framework.integration.with.mock.llms.and.deterministic.execution","name":"testing and validation framework integration with mock llms and deterministic execution","description":"Provides templates demonstrating testing patterns for LLM applications using LangChain's testing utilities, including mock LLMs for deterministic testing, fake embeddings for vector store testing, and callback-based assertion patterns. Templates show how to unit test chains and agents without calling real LLM providers, implement integration tests with recorded LLM responses (via VCR cassettes), and validate chain behavior across different scenarios. Supports both synchronous and asynchronous testing.","intents":["I need to unit test my LLM chains without calling real providers","I want to validate chain behavior deterministically across different inputs","I need to implement integration tests with recorded LLM responses"],"best_for":["teams building production LLM applications with quality requirements","organizations implementing CI/CD pipelines for LLM systems","developers validating chain behavior before deployment"],"limitations":["Mock LLMs don't capture real provider behavior — integration tests with real providers still needed","VCR cassettes require maintenance when LLM responses change","Testing agent behavior is complex — non-deterministic reasoning may require multiple test cases"],"requires":["Python 3.10+","LangChain >=1.2.6 with langchain-core >=1.2.7","pytest or similar testing framework","vcrpy for recording/replaying HTTP interactions (optional)"],"input_types":["chain or agent definitions","test inputs (prompts, user messages)","mock LLM responses or VCR cassettes"],"output_types":["test results (pass/fail)","assertion logs","coverage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_2","uri":"capability://text.generation.language.summarization.chain.templates.with.configurable.chunking.and.aggregation.strategies","name":"summarization chain templates with configurable chunking and aggregation strategies","description":"Offers pre-built templates for document summarization that handle long documents through configurable text splitting strategies (recursive character splitting, token-based splitting) and aggregation patterns (map-reduce, refine). Templates compose LangChain's text splitter abstractions with LLM chains to summarize documents larger than the LLM's context window, with support for both extractive and abstractive summarization approaches.","intents":["Summarize documents longer than LLM context window using map-reduce or refine patterns","Configure text splitting strategy (recursive, token-based) for domain-specific content","Generate multi-level summaries (chunk summaries → document summary → collection summary)","Preserve key information while reducing token count for downstream processing"],"best_for":["Content teams processing large document collections","Legal/compliance teams summarizing contracts or regulatory documents","Developers building document processing pipelines with token budget constraints"],"limitations":["Map-reduce pattern loses document structure and cross-chunk context — summaries may miss relationships between distant sections","Refine pattern is slower (sequential LLM calls per chunk) — documents with 100+ chunks may take minutes","Text splitting is heuristic-based — may split mid-sentence or break semantic boundaries in specialized domains","Summary quality degrades with document length — very long documents (10,000+ tokens) produce lower-quality summaries"],"requires":["Python 3.10+","LangChain 1.2.6+","langchain-text-splitters 1.1.0+ for chunking","LLM with sufficient context window (4k+ tokens recommended)"],"input_types":["long-form text documents","PDFs (via text extraction)","markdown or HTML content"],"output_types":["summarized text","hierarchical summaries (chunk + document level)","metadata (compression ratio, processing time)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_3","uri":"capability://tool.use.integration.sql.agent.templates.with.tool.based.database.query.generation.and.execution","name":"sql agent templates with tool-based database query generation and execution","description":"Provides templates for building SQL agents that use LLMs with tool-calling to generate and execute database queries against multiple SQL dialects (PostgreSQL, MySQL, SQLite, BigQuery). Agents use LangChain's tool abstraction to expose database schema introspection, query execution, and error handling as callable tools, enabling the LLM to iteratively refine queries based on execution feedback and schema information.","intents":["Build natural language interfaces to SQL databases without writing queries manually","Generate SQL queries dynamically based on user questions with schema awareness","Handle query errors gracefully by allowing LLM to refine queries based on error messages","Support multiple SQL dialects (PostgreSQL, MySQL, BigQuery) with dialect-specific query generation"],"best_for":["Teams building natural language database interfaces (text-to-SQL)","Business intelligence teams enabling non-technical users to query databases","Developers building chatbots with database access"],"limitations":["LLM query generation is unreliable on complex queries — joins across 5+ tables often produce incorrect SQL","Schema size is limited by context window — databases with 100+ tables may exceed token budget for schema introspection","No built-in query optimization — generated queries may be inefficient and timeout on large datasets","Security requires manual implementation — no automatic query sanitization or permission enforcement","Error recovery is limited — LLM may loop indefinitely on certain error types (e.g., permission denied)"],"requires":["Python 3.10+","LangChain 1.2.6+","SQLAlchemy 1.4+ for database abstraction","Database connection (PostgreSQL, MySQL, SQLite, BigQuery, etc.)","LLM with function calling support (OpenAI, Anthropic)"],"input_types":["natural language questions","database connection string","SQL schema (auto-introspected)"],"output_types":["generated SQL queries","query execution results (rows, metadata)","error messages and refinement attempts"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_4","uri":"capability://memory.knowledge.conversational.retrieval.templates.with.multi.turn.memory.and.context.management","name":"conversational retrieval templates with multi-turn memory and context management","description":"Provides templates for building multi-turn conversational systems that maintain chat history, retrieve relevant context from documents, and generate contextually-aware responses. Templates use LangChain's message history abstraction to persist conversation state, combine retrieval with chat models to ground responses in documents, and handle context window limits through configurable memory strategies (sliding window, summary-based compression).","intents":["Build chatbots that remember conversation history and reference previous messages","Retrieve relevant documents for each user message while maintaining conversation context","Generate responses grounded in both conversation history and retrieved documents","Handle long conversations by compressing or summarizing older messages to stay within context limits"],"best_for":["Teams building customer support chatbots with document access","Developers creating conversational document Q&A systems","Applications requiring multi-turn reasoning with external knowledge"],"limitations":["Memory persistence requires external storage — templates don't include built-in database for chat history","Context window limits force memory truncation — very long conversations (100+ turns) require aggressive compression","Retrieval happens per-message — no cross-turn retrieval optimization or caching","No built-in conversation branching or multi-user isolation — requires external session management","Memory compression (summarization) adds latency — each turn may require additional LLM call to compress history"],"requires":["Python 3.10+","LangChain 1.2.6+","Vector store for document retrieval (Pinecone, Weaviate, Chroma, FAISS)","Chat model (OpenAI, Anthropic, Groq)","External storage for chat history (optional but recommended for production)"],"input_types":["user messages (text)","documents for retrieval","conversation history (auto-managed)"],"output_types":["assistant responses (text)","retrieved document references","conversation metadata (turn count, tokens used)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface.and.fallback.routing","name":"multi-provider llm abstraction with unified interface and fallback routing","description":"Templates leverage LangChain's unified language model interface to abstract over multiple LLM providers (OpenAI, Anthropic, Groq, Ollama, local models) through a single Runnable API, enabling provider-agnostic chain definitions. Chains can be deployed with different LLM backends by changing configuration, with optional fallback routing that automatically switches providers if the primary provider fails or exceeds rate limits.","intents":["Write chain code once and deploy with different LLM providers (OpenAI → Anthropic) by changing config","Implement cost optimization by routing to cheaper models (Groq) for simple tasks and expensive models (GPT-4) for complex tasks","Build resilience by automatically falling back to alternative providers when primary provider fails","Support local model deployment (Ollama) alongside cloud providers for privacy-sensitive workloads"],"best_for":["Teams managing multiple LLM provider relationships","Cost-conscious developers optimizing LLM spend across providers","Organizations with privacy requirements needing local model support","Developers building multi-provider applications for resilience"],"limitations":["API compatibility varies across providers — some providers don't support function calling or JSON mode, requiring fallback to prompt-based approaches","Model capability differences require prompt tuning per provider — a prompt optimized for GPT-4 may fail with Llama 2","Rate limit handling is manual — fallback routing requires explicit configuration of rate limit thresholds","Cost tracking is not built-in — requires external logging to track spend across providers","Latency varies significantly across providers — no automatic latency-based routing"],"requires":["Python 3.10+","LangChain 1.2.6+","API keys for chosen LLM providers (OpenAI, Anthropic, Groq, etc.)","Ollama installation for local model support (optional)"],"input_types":["prompts (text)","provider configuration (JSON/YAML)","fallback provider list"],"output_types":["LLM responses (text, structured)","provider metadata (model used, latency, cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_6","uri":"capability://automation.workflow.lcel.based.chain.composition.with.declarative.pipeline.definition.and.optimization","name":"lcel-based chain composition with declarative pipeline definition and optimization","description":"Templates use LangChain Expression Language (LCEL) to define chains declaratively as composable Runnable objects, enabling automatic optimization, parallel execution, and streaming. LCEL chains are compiled to optimized execution graphs that can be serialized, cached, and deployed as REST APIs via LangServe, with built-in support for branching, conditional logic, and error handling through Runnable operators (pipe, map, batch, stream).","intents":["Define complex LLM chains declaratively without imperative control flow","Automatically optimize chain execution (parallelization, batching) without manual tuning","Stream responses from chains for real-time user feedback","Serialize and version chains for reproducibility and deployment"],"best_for":["Developers building complex multi-step LLM applications","Teams requiring reproducible, version-controlled chain definitions","Applications needing streaming responses for real-time UX","Developers deploying chains as REST APIs via LangServe"],"limitations":["LCEL syntax has a learning curve — developers familiar with imperative Python may find declarative style unfamiliar","Debugging LCEL chains is harder than imperative code — stack traces are less informative","Conditional logic in LCEL is limited — complex branching requires custom Runnable classes","Optimization is automatic but opaque — developers can't easily control parallelization strategy","Serialization support is incomplete — some custom Runnable classes don't serialize to JSON"],"requires":["Python 3.10+","LangChain 1.2.6+","Understanding of Runnable interface and LCEL syntax"],"input_types":["Runnable components (LLMs, retrievers, tools, custom functions)","LCEL chain definitions (Python code or serialized JSON)"],"output_types":["compiled execution graphs","streamed responses","batch results","serialized chain definitions"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_7","uri":"capability://safety.moderation.callback.and.event.system.integration.for.observability.and.monitoring","name":"callback and event system integration for observability and monitoring","description":"Templates integrate LangChain's callback system to emit structured events at each step of chain execution (LLM calls, tool invocations, retrieval operations), enabling observability without modifying chain code. Callbacks can be routed to external systems (LangSmith for tracing, custom logging backends) to track latency, token usage, errors, and intermediate results, with built-in support for async callbacks that don't block chain execution.","intents":["Track LLM token usage and costs across chain execution","Debug chain behavior by inspecting intermediate results and LLM inputs/outputs","Monitor chain performance (latency, error rates) in production","Integrate with external observability platforms (LangSmith, Datadog, custom backends)"],"best_for":["Teams deploying LLM applications to production requiring observability","Developers debugging complex chains with multiple LLM calls","Cost-conscious teams tracking token usage across providers","Organizations with compliance requirements for audit logging"],"limitations":["Callback overhead adds latency — synchronous callbacks can add 10-50ms per chain step","Async callbacks require careful error handling — failures in callbacks don't propagate to chain execution","LangSmith integration requires API key and network access — not suitable for air-gapped environments","Callback filtering is manual — no built-in way to selectively enable callbacks for specific chain steps","Token counting is approximate — actual token usage may differ from callback estimates"],"requires":["Python 3.10+","LangChain 1.2.6+","LangSmith API key (optional, for cloud tracing)","External logging backend (optional, for custom callbacks)"],"input_types":["chain execution events (LLM calls, tool invocations, retrieval)","callback configuration (handlers, filters)"],"output_types":["structured event logs (JSON)","trace data (LangSmith format)","metrics (latency, tokens, errors)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_8","uri":"capability://text.generation.language.prompt.template.management.with.variable.interpolation.and.dynamic.composition","name":"prompt template management with variable interpolation and dynamic composition","description":"Templates use LangChain's PromptTemplate abstraction to define reusable prompt templates with variable placeholders, supporting dynamic prompt composition by injecting context, examples, and instructions at runtime. Templates support multiple formats (f-string, Jinja2) and can be chained together to build complex prompts, with built-in validation to ensure all required variables are provided before LLM invocation.","intents":["Define reusable prompt templates with variable placeholders for dynamic content injection","Compose complex prompts from multiple template fragments (system prompt + examples + user input)","Validate prompt completeness before LLM invocation to catch missing variables early","Version and manage prompts separately from application code for easier iteration"],"best_for":["Teams managing multiple prompt variants for A/B testing","Developers building prompt-heavy applications requiring template reuse","Organizations with prompt governance requirements"],"limitations":["Template syntax is limited — complex conditional logic requires custom PromptTemplate subclasses","No built-in prompt versioning — requires external system to track prompt changes","Variable validation is static — can't validate variable types or formats at template definition time","No built-in prompt optimization — developers must manually tune prompts for quality"],"requires":["Python 3.10+","LangChain 1.2.6+"],"input_types":["prompt template strings (with {variable} placeholders)","variable values (dict or kwargs)"],"output_types":["formatted prompt strings","validation errors (missing variables)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__cap_9","uri":"capability://data.processing.analysis.document.loader.and.text.splitter.abstraction.for.multi.format.ingestion","name":"document loader and text splitter abstraction for multi-format ingestion","description":"Templates leverage LangChain's document loader abstraction to ingest content from multiple sources (PDFs, web pages, databases, cloud storage) and text splitter abstraction to chunk documents using configurable strategies (recursive character splitting, token-based splitting, semantic splitting). Loaders and splitters are composable Runnable objects that can be chained together to build document processing pipelines, with support for metadata preservation and custom splitting logic.","intents":["Ingest documents from multiple sources (PDF, HTML, Markdown, databases) with a unified API","Split documents into chunks optimized for embedding and retrieval","Preserve document metadata (source, page number, author) through the processing pipeline","Build custom document processing pipelines by composing loaders and splitters"],"best_for":["Teams building document processing pipelines with multiple input formats","Developers preparing documents for RAG systems","Applications requiring metadata-aware document chunking"],"limitations":["PDF parsing is fragile — complex PDFs with images, tables, or unusual layouts often produce garbled text","Text splitting is heuristic-based — may split mid-sentence or break semantic boundaries in specialized domains","Metadata preservation is manual — requires custom loader implementation to extract and preserve domain-specific metadata","No built-in deduplication — duplicate documents from multiple sources aren't automatically detected","Encoding issues are common — documents with mixed encodings may produce corrupted text"],"requires":["Python 3.10+","LangChain 1.2.6+","langchain-text-splitters 1.1.0+","Format-specific dependencies (PyPDF for PDFs, BeautifulSoup for HTML, etc.)"],"input_types":["documents (PDF, HTML, Markdown, plain text)","document sources (file paths, URLs, database queries)","splitting configuration (chunk size, overlap, strategy)"],"output_types":["Document objects with text and metadata","chunked documents with source references"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-templates__headline","uri":"capability://app.builders.langchain.rag.template.collection","name":"langchain rag template collection","description":"A comprehensive collection of deployable templates for building applications using Retrieval-Augmented Generation (RAG) with various vector stores, enabling seamless integration of LLMs into workflows.","intents":["best RAG framework","RAG templates for LLM applications","deployable templates for conversational retrieval","LangChain templates for SQL agents","best practices for RAG implementation"],"best_for":["developers building LLM applications","teams implementing RAG solutions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["app-builders","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","LangChain 1.2.6+","LangServe for REST API deployment","API keys for chosen LLM provider (OpenAI, Anthropic, etc.)","Vector store credentials (Pinecone, Weaviate, or local FAISS setup)","Pydantic 2.7.4+ for schema definition","LLM with function calling or JSON mode support (OpenAI, Anthropic, Groq)","LLM with function calling support (OpenAI, Anthropic, Groq)","LangGraph 1.0.2+ for agent orchestration","LLM with function calling support"],"failure_modes":["Vector store selection is compile-time configuration — runtime switching requires application restart","No built-in multi-tenancy or document isolation — requires external access control layer","Embedding model selection is fixed per template — no dynamic model switching","Templates assume synchronous retrieval — no streaming retrieval support out-of-box","Schema complexity is limited by LLM context window and token budget — deeply nested schemas may exceed practical limits","Extraction accuracy depends on LLM capability — smaller models (Llama 2) perform worse than GPT-4 on complex schemas","Retry logic adds latency — failed extractions may require 2-3 LLM calls before succeeding","No built-in handling of partial extraction — if one field fails validation, entire extraction fails","LLM function calling quality varies — some models (Llama 2) struggle with complex schemas","Schema size is limited by context window — tools with many parameters may exceed token budget","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","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=langchain-templates","compare_url":"https://unfragile.ai/compare?artifact=langchain-templates"}},"signature":"SpBasfWxEyMtjjqYJz9XnYZ5n1aafvEqpOoTiczrkthEWQjjfCiMemNrnZZrA5PY8WXHIzOgFC0Zok4LzxyEAw==","signedAt":"2026-06-20T03:40:41.527Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-templates","artifact":"https://unfragile.ai/langchain-templates","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-templates","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"}}