{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langchain","slug":"pypi-langchain","name":"langchain","type":"framework","url":"https://pypi.org/project/langchain/","page_url":"https://unfragile.ai/pypi-langchain","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langchain__cap_0","uri":"capability://automation.workflow.runnable.interface.composition.with.lcel.langchain.expression.language","name":"runnable interface composition with lcel (langchain expression language)","description":"LangChain provides a unified Runnable abstraction that enables declarative composition of LLM workflows through a pipe-based syntax (LCEL). Components like prompts, models, and parsers implement the Runnable interface with invoke(), stream(), and batch() methods, allowing developers to chain operations without imperative glue code. The framework handles async/sync duality, streaming propagation, and parallel execution automatically through the Runnable protocol.","intents":["I want to compose LLM calls, prompt templates, and output parsers into a single declarative pipeline without writing orchestration code","I need my LLM chains to support both synchronous and asynchronous execution without duplicating logic","I want to stream token-by-token responses from multi-step chains without buffering intermediate results"],"best_for":["Teams building modular LLM applications with reusable component libraries","Developers migrating from imperative callback-based chains to declarative pipelines","Applications requiring streaming responses across multiple processing steps"],"limitations":["Runnable composition adds ~50-100ms overhead per chain step due to abstraction layers and method dispatch","Debugging complex LCEL chains requires understanding the pipe operator semantics and Runnable protocol internals","Type hints in LCEL chains can be verbose and require careful generic parameter specification"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for schema validation"],"input_types":["Python objects (dicts, Pydantic models, strings)","Streaming iterables"],"output_types":["Python objects matching Runnable output schema","Async generators for streaming","Batch results as lists"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_1","uri":"capability://tool.use.integration.multi.provider.language.model.abstraction.with.unified.interface","name":"multi-provider language model abstraction with unified interface","description":"LangChain abstracts OpenAI, Anthropic, Groq, Ollama, and 50+ other LLM providers through BaseLanguageModel and BaseChatModel classes, exposing a unified invoke/stream/batch interface regardless of underlying provider. Each provider integration handles authentication, request formatting, response parsing, and streaming protocol differences (SSE for OpenAI, custom formats for Anthropic) internally, allowing developers to swap providers with minimal code changes.","intents":["I want to build LLM applications that can switch between OpenAI, Anthropic, and open-source models without rewriting logic","I need to handle provider-specific features (like Anthropic's vision or tool_choice) while maintaining a common interface","I want to test my application against multiple models without duplicating chain definitions"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Applications requiring fallback providers or multi-model ensemble strategies","Developers building LLM frameworks that should remain provider-agnostic"],"limitations":["Provider-specific features (vision, function calling schemas, structured output formats) require conditional logic or adapter patterns","Streaming behavior varies across providers (some buffer tokens, others stream character-by-character), affecting latency profiles","Rate limiting and quota management must be implemented per-provider as LangChain provides no unified rate limiting layer"],"requires":["Python 3.10+","langchain-core>=1.2.7","Provider-specific API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Optional: langchain-openai, langchain-anthropic, langchain-groq packages for integrations"],"input_types":["Prompt strings","Message lists (BaseMessage objects)","Structured inputs with tool/function schemas"],"output_types":["Text completions (AIMessage objects)","Streaming token iterators","Structured outputs (when using response_format or tool_choice)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_10","uri":"capability://automation.workflow.configuration.and.runtime.control.with.environment.based.secrets.management","name":"configuration and runtime control with environment-based secrets management","description":"LangChain uses Pydantic's ConfigDict and environment variable loading to manage API keys, model parameters, and runtime configuration. Developers configure models through environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY) or explicit parameters, with Pydantic validation ensuring type safety. The framework supports lazy initialization and parameter overrides at runtime.","intents":["I want to configure LLM providers and models through environment variables without hardcoding secrets","I need to override model parameters (temperature, max_tokens) at runtime for different use cases","I want to validate configuration at startup to catch missing API keys or invalid parameters early"],"best_for":["Teams deploying LLM applications across environments (dev, staging, prod)","Applications requiring flexible model configuration without code changes","Developers managing multiple API keys and provider credentials"],"limitations":["Environment variable naming is provider-specific; no unified convention across all providers","Configuration validation happens at instantiation, not at startup; missing secrets may not be caught until first LLM call","No built-in secret rotation or credential refresh; requires external secret management systems"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for configuration validation","Environment variables for API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)"],"input_types":["Environment variables","Configuration dictionaries","Pydantic model instances"],"output_types":["Configured LLM instances","Validated configuration objects","Runtime parameter overrides"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_11","uri":"capability://automation.workflow.caching.and.memoization.for.llm.calls.and.embeddings","name":"caching and memoization for llm calls and embeddings","description":"LangChain provides caching layers (InMemoryCache, RedisCache, SQLiteCache) that memoize LLM responses and embedding results based on input hash. The framework integrates caching transparently into Runnable chains through the cache parameter. Caching reduces API costs and latency for repeated queries, with configurable TTL and eviction policies.","intents":["I want to cache LLM responses to reduce API costs and latency for repeated queries","I need to cache embeddings to avoid re-embedding the same documents","I want to use distributed caching (Redis) for multi-instance deployments"],"best_for":["Applications with repetitive queries or high-volume traffic","Teams optimizing LLM API costs","Distributed systems requiring shared caching across instances"],"limitations":["Cache invalidation is manual; no automatic cache busting when prompts or models change","In-memory caching is not suitable for large-scale deployments; Redis caching adds network latency","Cache key is based on input hash; semantic similarity is not considered (similar queries won't hit cache)"],"requires":["Python 3.10+","langchain-core>=1.2.7","Optional: redis for distributed caching","Optional: sqlalchemy for SQLite caching"],"input_types":["LLM prompts and messages","Embedding inputs","Cache configuration (TTL, max size)"],"output_types":["Cached LLM responses","Cached embeddings","Cache hit/miss metrics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_12","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.chain.composition.with.document.context","name":"retrieval-augmented generation (rag) chain composition with document context","description":"LangChain provides retriever abstractions and pre-built RAG patterns that combine document retrieval with LLM generation. Developers compose retriever Runnables with prompt templates and LLMs to build RAG chains that fetch relevant documents and pass them as context. The framework handles document formatting, context window management, and result ranking automatically.","intents":["I want to build RAG applications that retrieve relevant documents and pass them to LLMs for grounded responses","I need to compose retrievers with prompts and models into a single RAG chain without orchestration code","I want to manage context window limits and document ranking in RAG chains"],"best_for":["Teams building question-answering systems over document collections","Applications requiring grounded responses with source attribution","Developers implementing retrieval-based chatbots"],"limitations":["Retrieval quality depends on embedding model and vector store; no built-in evaluation or ranking optimization","Context window limits require manual document selection or summarization; no automatic context pruning","Multi-hop retrieval (retrieving documents that reference other documents) requires custom logic"],"requires":["Python 3.10+","langchain-core>=1.2.7","Embeddings provider (OpenAI, Cohere, local model)","Vector store (Pinecone, Weaviate, Chroma, FAISS)","LLM with sufficient context window"],"input_types":["User queries","Document collections","Retriever configuration"],"output_types":["Retrieved documents with similarity scores","LLM responses with document context","Source attribution"],"categories":["memory-knowledge","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_13","uri":"capability://automation.workflow.batch.processing.and.parallel.execution.with.async.support","name":"batch processing and parallel execution with async support","description":"LangChain's Runnable interface provides batch() and stream() methods that enable parallel processing of multiple inputs and streaming of results. The framework handles async/sync duality automatically, allowing developers to process large datasets without explicit parallelization code. Batch processing respects rate limits and provider quotas through configurable concurrency.","intents":["I want to process large batches of documents or queries in parallel without writing async orchestration code","I need to stream results from long-running chains without buffering intermediate outputs","I want to respect API rate limits while maximizing throughput in batch processing"],"best_for":["Applications processing large document collections (embeddings, classification)","Batch jobs requiring parallel LLM inference","Developers building data pipelines with streaming results"],"limitations":["Batch processing adds overhead for small datasets; not suitable for single-query latency optimization","Rate limiting is provider-specific; no unified rate limiting across providers","Streaming results requires careful memory management for large batches; no built-in backpressure handling"],"requires":["Python 3.10+","langchain-core>=1.2.7","Async runtime (asyncio) for async batch processing","Optional: tenacity>=8.1.0 for retry logic"],"input_types":["Lists of inputs (documents, queries, etc.)","Async iterables","Batch configuration (concurrency, timeout)"],"output_types":["Lists of results","Async generators for streaming","Batch processing metrics (throughput, latency)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_14","uri":"capability://automation.workflow.retry.and.error.handling.with.exponential.backoff.and.fallback.strategies","name":"retry and error handling with exponential backoff and fallback strategies","description":"LangChain integrates tenacity for automatic retry logic with exponential backoff, enabling resilient LLM applications that recover from transient failures. The framework supports custom retry predicates, fallback models, and error callbacks. Retry logic is transparent to developers through Runnable composition.","intents":["I want my LLM chains to automatically retry on transient failures (rate limits, timeouts) without explicit error handling","I need to fall back to alternative models or providers if the primary provider fails","I want to implement custom retry logic based on error types (rate limit vs authentication error)"],"best_for":["Production LLM applications requiring high availability","Applications using multiple LLM providers for redundancy","Teams building resilient data pipelines"],"limitations":["Retry logic can significantly increase latency for failing requests; exponential backoff may be too aggressive for some use cases","Fallback strategies require manual configuration; no automatic provider selection based on cost or latency","Retry logic is transparent, making debugging failures difficult without detailed logging"],"requires":["Python 3.10+","langchain-core>=1.2.7","tenacity>=8.1.0 for retry logic"],"input_types":["Runnable chains","Retry configuration (max_retries, backoff_factor)","Custom retry predicates"],"output_types":["Successful results after retries","Fallback results from alternative providers","Retry metrics (attempt count, total latency)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_2","uri":"capability://tool.use.integration.schema.based.tool.calling.and.function.execution.with.multi.provider.support","name":"schema-based tool calling and function execution with multi-provider support","description":"LangChain provides a BaseTool abstraction and ToolCall message type that standardizes function calling across OpenAI, Anthropic, and other providers. Developers define tools as Pydantic models with descriptions, and LangChain automatically converts these to provider-specific schemas (OpenAI functions, Anthropic tools, Claude XML). The framework handles tool invocation, result formatting, and multi-turn tool use loops through AgentExecutor or custom middleware.","intents":["I want to define tools once and have them work across OpenAI, Anthropic, and other providers without schema translation","I need my LLM to call functions with validated arguments and handle tool results in a structured way","I want to build agents that can use tools iteratively, with the framework managing the tool-use loop"],"best_for":["Teams building multi-provider agents with consistent tool interfaces","Applications requiring structured function calling with argument validation","Developers implementing ReAct or tool-use agent patterns"],"limitations":["Tool schemas must be Pydantic models, adding complexity for dynamic or unstructured tool definitions","Provider-specific tool features (like Anthropic's tool_choice or OpenAI's parallel function calling) require conditional logic","Tool execution is not automatically retried on validation errors; developers must implement retry logic in agents"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for tool schema definition","Provider integration package (langchain-openai, langchain-anthropic, etc.)"],"input_types":["Pydantic model definitions (tool schemas)","Tool result objects (text, structured data, or errors)"],"output_types":["ToolCall message objects with tool_name and arguments","Tool results formatted as ToolMessage objects","Agent action/observation tuples for agentic loops"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_3","uri":"capability://text.generation.language.prompt.template.composition.with.variable.interpolation.and.partial.binding","name":"prompt template composition with variable interpolation and partial binding","description":"LangChain provides PromptTemplate and ChatPromptTemplate classes that support Jinja2-style variable interpolation, partial binding (freezing some variables while leaving others dynamic), and composition with other Runnables. Templates validate required variables at instantiation and support format_prompt() for rendering with context, enabling reusable prompt patterns across applications.","intents":["I want to define reusable prompt templates with variables that can be filled in at runtime","I need to compose multiple prompt templates (system + user messages) into a single ChatPromptTemplate","I want to partially bind some variables in a prompt while leaving others dynamic for different invocations"],"best_for":["Teams managing multiple prompt variants across different use cases","Applications requiring prompt versioning and A/B testing","Developers building prompt libraries or frameworks"],"limitations":["Template syntax is limited to Jinja2-style variable substitution; complex logic requires custom filters or separate preprocessing","No built-in prompt versioning or experiment tracking; requires external tools like LangSmith","Variable validation happens at instantiation, not at render time, so missing variables are caught early but dynamic validation is not supported"],"requires":["Python 3.10+","langchain-core>=1.2.7","Jinja2 for template rendering (included as dependency)"],"input_types":["Template strings with {variable} placeholders","Dictionaries with variable values","Pydantic models for structured inputs"],"output_types":["Rendered prompt strings","ChatPromptValue objects (list of BaseMessage objects)","Runnable chains combining templates with models"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_4","uri":"capability://data.processing.analysis.document.chunking.and.text.splitting.with.semantic.awareness","name":"document chunking and text splitting with semantic awareness","description":"LangChain provides text splitters (RecursiveCharacterTextSplitter, MarkdownHeaderTextSplitter, etc.) that break documents into chunks while preserving semantic boundaries. Splitters support configurable chunk size, overlap, and metadata preservation, with language-specific variants for code and markdown. The langchain-text-splitters package provides optimized implementations that maintain context across chunks.","intents":["I want to split long documents into chunks that fit within LLM context windows while preserving semantic meaning","I need to chunk code or markdown documents while respecting language structure (functions, headers, etc.)","I want to maintain metadata (source, line numbers, section headers) across chunks for retrieval and attribution"],"best_for":["RAG applications requiring document preprocessing before embedding and retrieval","Teams building document processing pipelines with semantic awareness","Applications handling mixed content types (code, markdown, plain text)"],"limitations":["Semantic splitting is heuristic-based (splitting on newlines, code blocks) and may not preserve meaning for all document types","No built-in support for language-specific parsing beyond code and markdown; custom splitters required for domain-specific formats","Chunk overlap can lead to redundant embeddings and increased storage costs in vector databases"],"requires":["Python 3.10+","langchain-text-splitters>=1.1.0","Optional: tiktoken for token-based splitting, tree-sitter for code parsing"],"input_types":["Document strings","Document objects with metadata","Code files (Python, JavaScript, etc.)","Markdown files"],"output_types":["List of Document objects with content and metadata","Chunks with preserved source attribution","Token counts for each chunk"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_5","uri":"capability://memory.knowledge.embedding.generation.and.vector.store.integration.with.multi.provider.support","name":"embedding generation and vector store integration with multi-provider support","description":"LangChain abstracts embedding providers (OpenAI, Cohere, HuggingFace, Ollama) through a Embeddings base class, and integrates with vector stores (Pinecone, Weaviate, Chroma, FAISS) through a VectorStore interface. Developers can embed documents, query for similar vectors, and build retrieval chains without vendor lock-in. The framework handles batch embedding, caching, and lazy loading of vector stores.","intents":["I want to embed documents using OpenAI, Cohere, or local models and store them in a vector database","I need to query a vector store for semantically similar documents without writing database-specific code","I want to build RAG chains that retrieve documents and pass them to LLMs without manual vector store orchestration"],"best_for":["Teams building RAG applications with flexible embedding and vector store choices","Applications requiring semantic search across document collections","Developers prototyping with local embeddings before moving to cloud providers"],"limitations":["Embedding quality varies significantly across providers; no built-in evaluation or comparison tools","Vector store integrations are shallow abstractions; advanced features (filtering, metadata search) require provider-specific code","Batch embedding can be slow for large document collections; no built-in parallelization or progress tracking"],"requires":["Python 3.10+","langchain-core>=1.2.7","Embedding provider API key (OPENAI_API_KEY, COHERE_API_KEY, etc.) or local model","Vector store package (langchain-pinecone, langchain-chroma, etc.)"],"input_types":["Document objects with text content","Query strings","Metadata filters (provider-specific)"],"output_types":["Embedding vectors (1536-dim for OpenAI, variable for others)","Retrieved Document objects with similarity scores","Retriever Runnable objects for composition"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_6","uri":"capability://text.generation.language.message.and.content.handling.with.multi.modal.support","name":"message and content handling with multi-modal support","description":"LangChain provides a BaseMessage hierarchy (HumanMessage, AIMessage, SystemMessage, ToolMessage) that standardizes conversation history across providers. The framework supports multi-modal content through ContentBlock objects (text, image, tool calls), enabling vision-capable models like Claude and GPT-4V to process images alongside text. Message serialization and deserialization handle provider-specific formats automatically.","intents":["I want to build multi-turn conversations with consistent message types across different LLM providers","I need to handle images in prompts and maintain conversation history with vision-capable models","I want to serialize and deserialize conversation history for persistence or logging"],"best_for":["Teams building conversational AI applications with multi-turn context","Applications using vision-capable models (Claude, GPT-4V, Gemini)","Developers implementing chat interfaces with persistent conversation history"],"limitations":["Image handling is provider-specific; OpenAI uses base64 encoding while Anthropic uses URL references, requiring adapter code","Message history grows unbounded; no built-in summarization or pruning for long conversations","Tool message formatting varies across providers; conditional logic required for multi-provider tool use"],"requires":["Python 3.10+","langchain-core>=1.2.7","Provider integration package for multi-modal support (langchain-openai, langchain-anthropic)"],"input_types":["Message objects (HumanMessage, AIMessage, etc.)","Text strings (auto-converted to HumanMessage)","Image URLs or base64-encoded images","Tool call and result objects"],"output_types":["Message lists representing conversation history","Serialized message JSON for persistence","Provider-specific message formats (OpenAI ChatCompletionMessageParam, etc.)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_7","uri":"capability://automation.workflow.callback.and.event.system.for.observability.and.monitoring","name":"callback and event system for observability and monitoring","description":"LangChain provides a BaseCallbackHandler interface that enables developers to hook into LLM execution events (start, end, error, token streaming) for logging, monitoring, and debugging. The framework integrates with LangSmith for production observability, tracing, and prompt versioning. Callbacks propagate through the Runnable chain automatically, enabling end-to-end visibility without instrumentation code.","intents":["I want to log and monitor LLM calls, token usage, and latency without modifying my application code","I need to trace execution through multi-step chains to debug failures and understand performance bottlenecks","I want to integrate with LangSmith for production observability and prompt versioning"],"best_for":["Teams running LLM applications in production requiring observability","Developers debugging complex chains and agent behavior","Applications requiring audit trails and cost tracking"],"limitations":["Callback overhead can add 10-50ms per chain step depending on handler complexity","LangSmith integration requires API key and incurs costs for production tracing","Custom callbacks must implement the full BaseCallbackHandler interface; no lightweight decorator-based alternative"],"requires":["Python 3.10+","langchain-core>=1.2.7","Optional: langsmith>=0.3.45 for production tracing","Optional: LangSmith API key for cloud observability"],"input_types":["Runnable chains with callback handlers","Custom callback implementations"],"output_types":["Structured event logs (start, end, error events)","Token usage and latency metrics","Traces in LangSmith dashboard"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_8","uri":"capability://planning.reasoning.agent.creation.and.middleware.architecture.with.langgraph.integration","name":"agent creation and middleware architecture with langgraph integration","description":"LangChain provides AgentExecutor and agent creation utilities that orchestrate tool-using loops, but delegates stateful agent logic to LangGraph. The framework defines agent middleware patterns (planning, tool calling, result processing) that can be composed into custom agents. LangGraph handles state management, branching, and multi-step reasoning, while LangChain provides the LLM and tool abstractions.","intents":["I want to build agents that can use tools iteratively to solve problems without writing orchestration code","I need to implement custom agent logic (planning, reflection, tool selection) with clear middleware patterns","I want to debug agent execution by inspecting state transitions and decision points"],"best_for":["Teams building autonomous agents with tool use and reasoning","Applications requiring custom agent logic beyond ReAct pattern","Developers implementing multi-step reasoning or planning agents"],"limitations":["Agent execution can be slow due to multiple LLM calls per step; no built-in optimization for reducing calls","Tool selection errors (hallucinated tools, incorrect arguments) require explicit error handling in agent loops","Debugging agent behavior requires understanding both LangChain tool abstractions and LangGraph state management"],"requires":["Python 3.10+","langchain>=1.2.6","langgraph>=1.0.2 for stateful agents","Tool definitions (BaseTool subclasses)","LLM with tool calling support (OpenAI, Anthropic, etc.)"],"input_types":["User query or task description","Tool definitions","System prompts for agent behavior"],"output_types":["Final agent response (text or structured)","Execution trace with tool calls and results","Agent state at each step"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain__cap_9","uri":"capability://data.processing.analysis.structured.output.parsing.and.response.format.validation","name":"structured output parsing and response format validation","description":"LangChain provides output parsers (JsonOutputParser, PydanticOutputParser, StructuredOutputParser) that validate and parse LLM responses into structured formats. The framework integrates with provider-specific structured output features (OpenAI's response_format, Anthropic's tool_use) to ensure valid outputs. Parsers handle malformed responses with retry logic and fallback strategies.","intents":["I want to extract structured data (JSON, Pydantic models) from LLM responses with validation","I need to ensure LLM outputs conform to a schema without manual parsing and error handling","I want to use provider-specific structured output features (OpenAI JSON mode, Anthropic tool_use) transparently"],"best_for":["Applications requiring structured data extraction from LLM outputs","Teams building APIs that consume LLM responses as structured inputs","Developers implementing multi-step chains where each step requires validated output"],"limitations":["Parser retry logic can add significant latency if LLM repeatedly produces invalid output","Provider-specific structured output features (OpenAI JSON mode) may not be available for all models","Complex nested schemas may require custom parser implementations"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for schema definition","Optional: Provider integration for structured output support"],"input_types":["LLM response strings","Pydantic model schemas","JSON schema definitions"],"output_types":["Parsed Python objects (dicts, Pydantic models)","Validated structured data","Retry attempts on parse failure"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for schema validation","Provider-specific API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Optional: langchain-openai, langchain-anthropic, langchain-groq packages for integrations","Pydantic>=2.7.4 for configuration validation","Environment variables for API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Optional: redis for distributed caching","Optional: sqlalchemy for SQLite caching","Embeddings provider (OpenAI, Cohere, local model)"],"failure_modes":["Runnable composition adds ~50-100ms overhead per chain step due to abstraction layers and method dispatch","Debugging complex LCEL chains requires understanding the pipe operator semantics and Runnable protocol internals","Type hints in LCEL chains can be verbose and require careful generic parameter specification","Provider-specific features (vision, function calling schemas, structured output formats) require conditional logic or adapter patterns","Streaming behavior varies across providers (some buffer tokens, others stream character-by-character), affecting latency profiles","Rate limiting and quota management must be implemented per-provider as LangChain provides no unified rate limiting layer","Environment variable naming is provider-specific; no unified convention across all providers","Configuration validation happens at instantiation, not at startup; missing secrets may not be caught until first LLM call","No built-in secret rotation or credential refresh; requires external secret management systems","Cache invalidation is manual; no automatic cache busting when prompts or models change","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:11.786Z","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=pypi-langchain","compare_url":"https://unfragile.ai/compare?artifact=pypi-langchain"}},"signature":"ghHiZZfaf49W4UFY9iFcMBHmEbg2p2hLile92QjP8ktU3m8fwHjeSuoSmCAJcIE1UCUJRC9fAwQ6eY1UeVPVAg==","signedAt":"2026-06-19T17:49:59.688Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langchain","artifact":"https://unfragile.ai/pypi-langchain","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-langchain","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"}}