{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langchain-core","slug":"pypi-langchain-core","name":"langchain-core","type":"framework","url":"https://pypi.org/project/langchain-core/","page_url":"https://unfragile.ai/pypi-langchain-core","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langchain-core__cap_0","uri":"capability://automation.workflow.runnable.interface.composition.with.lcel.langchain.expression.language","name":"runnable interface composition with lcel (langchain expression language)","description":"Provides a unified Runnable abstraction that enables declarative chaining of LLM components (models, prompts, tools, retrievers) through operator overloading and pipe syntax. LCEL compiles chains into optimized execution graphs with automatic batching, streaming, and async support. The pattern uses Python's __or__ operator to create composable pipelines that decouple component logic from orchestration, enabling both synchronous and asynchronous execution paths with identical code.","intents":["I want to chain LLM calls with prompts and tools without writing orchestration boilerplate","I need to build complex multi-step workflows that support both sync and async execution","I want to enable streaming responses from composed chains without rewriting the pipeline","I need to batch process multiple inputs through the same chain efficiently"],"best_for":["LLM application developers building production chains","Teams migrating from imperative orchestration to declarative composition","Developers needing streaming-first architectures"],"limitations":["Operator overloading syntax requires Python 3.10+ for full type hint support","Complex conditional branching requires explicit RunnableBranch or RunnableIf wrappers rather than native if/else","Debugging composed chains requires understanding LCEL compilation to execution graph","State management across chain steps requires explicit context passing via RunnableConfig"],"requires":["Python 3.10+","pydantic>=2.7.4 for schema validation","langsmith>=0.3.45 for tracing and debugging"],"input_types":["dict (key-value context)","BaseMessage (chat history)","str (raw text)","structured objects with Pydantic models"],"output_types":["str (text response)","BaseMessage (chat message)","dict (structured output)","AsyncIterator (streaming chunks)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_1","uri":"capability://tool.use.integration.language.model.abstraction.with.provider.agnostic.interface","name":"language model abstraction with provider-agnostic interface","description":"Defines BaseLanguageModel and ChatModel abstract base classes that normalize API differences across OpenAI, Anthropic, Groq, Ollama, and other LLM providers through a unified invoke/stream/batch interface. Each provider integration implements the same Runnable protocol, allowing chains to swap models without code changes. The abstraction handles token counting, model configuration (temperature, max_tokens), and response parsing through a consistent schema.","intents":["I want to build chains that work with multiple LLM providers interchangeably","I need to compare model performance without rewriting my application logic","I want to fall back to a different provider if one is unavailable","I need to track token usage across different model families"],"best_for":["Teams evaluating multiple LLM providers","Applications requiring provider redundancy or cost optimization","Developers building multi-model comparison tools"],"limitations":["Provider-specific features (vision, function calling variants) require conditional logic or provider-specific subclasses","Token counting is approximate for some providers and requires model-specific implementations","Streaming behavior differs subtly between providers (chunk boundaries, error handling)","Rate limiting and retry logic must be configured per provider"],"requires":["Python 3.10+","API keys for target LLM provider(s)","Provider-specific integration package (langchain-openai, langchain-anthropic, etc.)"],"input_types":["list[BaseMessage] (chat history)","str (raw prompt)","dict (structured input with Pydantic schema)"],"output_types":["BaseMessage (single response)","AsyncIterator[BaseMessage] (streaming chunks)","list[BaseMessage] (batch results)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_10","uri":"capability://automation.workflow.configuration.and.runtime.control.through.runnableconfig","name":"configuration and runtime control through runnableconfig","description":"Provides RunnableConfig dataclass that enables fine-grained control over Runnable execution including callbacks, tags, metadata, recursion limits, and timeout settings. Config propagates through composed chains automatically, allowing global configuration of tracing, error handling, and resource limits without modifying chain code. Supports both context-based configuration (via context managers) and explicit parameter passing.","intents":["I want to add tracing and callbacks to chains without modifying the chain definition","I need to set timeouts and recursion limits to prevent resource exhaustion","I want to tag chain executions for monitoring and debugging","I need to pass runtime configuration through composed chains"],"best_for":["Developers building production LLM applications","Teams requiring fine-grained execution control","Applications with strict resource constraints"],"limitations":["Config propagation through deeply nested chains can be difficult to debug","Timeout enforcement depends on underlying implementation — not guaranteed across all Runnables","Recursion limits are process-wide and may conflict with other code","No built-in config validation — invalid settings fail at runtime"],"requires":["Python 3.10+","Understanding of Runnable execution model"],"input_types":["dict (config parameters)"],"output_types":["RunnableConfig (typed configuration object)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_11","uri":"capability://automation.workflow.batch.processing.and.streaming.with.automatic.optimization","name":"batch processing and streaming with automatic optimization","description":"Enables batch and stream execution modes on any Runnable through batch() and stream() methods that automatically optimize execution strategy. Batch mode uses provider-specific batch APIs when available (e.g., OpenAI batch API) to reduce costs and latency. Stream mode returns async iterators that yield results incrementally, enabling real-time response handling. The system automatically selects the optimal execution path based on Runnable type and configuration.","intents":["I want to process multiple inputs efficiently without sequential execution","I need to stream LLM responses to users in real-time","I want to use provider batch APIs automatically without manual configuration","I need to handle backpressure when streaming large result sets"],"best_for":["Developers building high-throughput applications","Teams requiring real-time response streaming","Applications with cost-sensitive batch processing"],"limitations":["Batch API support varies by provider — not all providers offer batch endpoints","Streaming adds latency for first token due to async overhead","Batch processing requires buffering inputs in memory — not suitable for infinite streams","Error handling in batch mode is different from streaming (batch errors are returned per item)"],"requires":["Python 3.10+","Async runtime (asyncio)","Provider support for batch or streaming APIs"],"input_types":["list (multiple inputs for batch)","single input (for stream)"],"output_types":["list (batch results)","AsyncIterator (streaming chunks)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_12","uri":"capability://tool.use.integration.dependency.injection.and.provider.integration.through.optional.packages","name":"dependency injection and provider integration through optional packages","description":"Uses optional dependency pattern where core abstractions (BaseLanguageModel, BaseTool, BaseRetriever) are defined in langchain-core, while provider-specific implementations live in separate packages (langchain-openai, langchain-anthropic, etc.). This enables modular installation and prevents bloated dependencies. Integration packages implement the same Runnable interface, allowing seamless swapping. The system uses lazy imports and version pinning to ensure compatibility.","intents":["I want to install only the providers I need without bloating my dependencies","I need to ensure version compatibility between core and provider packages","I want to add custom provider implementations without modifying core","I need to support multiple providers in the same application"],"best_for":["Teams with strict dependency management","Developers building provider-agnostic applications","Applications requiring custom provider implementations"],"limitations":["Modular structure requires understanding package dependencies and version pinning","Custom provider implementations must follow Runnable interface conventions","Version mismatches between core and provider packages can cause subtle bugs","No automatic dependency resolution — users must install required providers explicitly"],"requires":["Python 3.10+","langchain-core>=1.2.7","Provider-specific packages (langchain-openai, langchain-anthropic, etc.)","Strict version pinning (pydantic>=2.7.4, <3.0.0)"],"input_types":["provider configuration (API keys, model names)"],"output_types":["provider-specific Runnable implementations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_2","uri":"capability://text.generation.language.message.and.content.type.system.with.multimodal.support","name":"message and content type system with multimodal support","description":"Provides a type hierarchy (BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage) that standardizes conversation history representation across providers. Supports multimodal content through ContentBlock unions that can contain text, images, tool calls, and tool results. The system uses Pydantic discriminated unions to ensure type safety and enable provider-specific serialization (e.g., OpenAI's image_url format vs Anthropic's base64 encoding).","intents":["I want to maintain conversation history in a provider-agnostic format","I need to handle multimodal inputs (text + images) in chat applications","I want to track tool calls and results within conversation context","I need to serialize messages to different provider formats automatically"],"best_for":["Chat application developers managing conversation state","Teams building multimodal LLM applications","Developers implementing agent memory systems"],"limitations":["Image handling requires provider-specific format conversion (no automatic transcoding)","Tool result serialization must match provider expectations (some providers require specific JSON structures)","Message history can grow unbounded — requires explicit truncation or summarization strategies","Pydantic validation adds ~5-10ms per message for complex content blocks"],"requires":["Python 3.10+","pydantic>=2.7.4","langsmith>=0.3.45 for message tracing"],"input_types":["str (text content)","dict (image with URL or base64)","ToolCall (function invocation)","ToolResult (function output)"],"output_types":["BaseMessage (typed message object)","list[BaseMessage] (conversation history)","dict (provider-specific serialization)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_3","uri":"capability://tool.use.integration.tool.and.function.calling.schema.generation.with.validation","name":"tool and function calling schema generation with validation","description":"Converts Python functions and Pydantic models into JSON Schema representations that LLM providers can use for function calling. The system uses Pydantic's schema generation to create provider-compatible schemas (OpenAI, Anthropic, Groq formats) with automatic docstring parsing for descriptions. BaseTool abstract class enables custom tool implementations with built-in error handling, argument validation, and async support through the Runnable interface.","intents":["I want to expose Python functions to LLMs without manually writing JSON schemas","I need to validate tool arguments before execution","I want to handle tool errors gracefully and return them to the LLM for recovery","I need to support both sync and async tool execution in agent loops"],"best_for":["Developers building LLM agents with tool use","Teams implementing ReAct or similar agent patterns","Applications requiring structured function calling"],"limitations":["Schema generation from docstrings is heuristic-based and may require manual refinement for complex types","Provider-specific schema variants (OpenAI vs Anthropic) require conditional logic or separate tool definitions","Tool execution errors must be caught and formatted as ToolMessage for LLM consumption — no automatic error recovery","Recursive or circular type references in Pydantic models may cause schema generation failures"],"requires":["Python 3.10+","pydantic>=2.7.4","Function or class with type hints and docstrings"],"input_types":["Python function with type hints","Pydantic BaseModel","dict (tool arguments from LLM)"],"output_types":["dict (JSON Schema)","str (tool result)","ToolMessage (formatted for LLM)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_4","uri":"capability://text.generation.language.prompt.template.system.with.variable.interpolation.and.formatting","name":"prompt template system with variable interpolation and formatting","description":"Provides PromptTemplate and ChatPromptTemplate classes that enable parameterized prompt construction with variable substitution, type validation, and partial application. Templates use Jinja2-style syntax with Pydantic validation to ensure all required variables are provided before execution. The system integrates with the Runnable interface, allowing prompts to be composed with models and other components in chains.","intents":["I want to create reusable prompt templates with validated variables","I need to partially apply prompts with some variables and defer others","I want to format prompts consistently across my application","I need to compose prompts with models in LCEL chains"],"best_for":["Developers managing multiple prompt variants","Teams building prompt engineering workflows","Applications requiring consistent prompt formatting"],"limitations":["Jinja2 syntax is limited compared to full template languages — no complex logic or filters","Variable validation happens at template creation time, not at render time, so missing variables cause errors during chain execution","No built-in versioning or A/B testing framework — requires external tooling","Large templates with many variables can be difficult to debug"],"requires":["Python 3.10+","pydantic>=2.7.4"],"input_types":["str (template string with {variable} placeholders)","dict (variable values)","BaseMessage (for ChatPromptTemplate)"],"output_types":["str (formatted prompt)","list[BaseMessage] (formatted chat messages)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_5","uri":"capability://data.processing.analysis.document.chunking.and.text.splitting.with.semantic.awareness","name":"document chunking and text splitting with semantic awareness","description":"Provides TextSplitter implementations (RecursiveCharacterTextSplitter, MarkdownHeaderTextSplitter, CodeTextSplitter) that break documents into chunks while preserving semantic boundaries and metadata. Splitters support configurable chunk size, overlap, and separator strategies. The system includes token counting integration to split by token count rather than character count, and preserves document metadata through chunk attribution.","intents":["I want to split long documents into LLM-friendly chunks without breaking semantic meaning","I need to preserve document structure (headers, code blocks) when chunking","I want to chunk by token count to fit within model context windows","I need to maintain metadata (source, page number) through the chunking process"],"best_for":["Developers building RAG systems","Teams processing documents for vector embeddings","Applications requiring document-aware chunking"],"limitations":["Semantic awareness is limited to structural markers (headers, code blocks) — no true semantic understanding","Token counting requires model-specific tokenizers and adds latency","Overlap between chunks can lead to duplicate embeddings and increased storage","No built-in handling of tables, images, or other structured content"],"requires":["Python 3.10+","langchain-text-splitters package","Optional: tiktoken for token counting"],"input_types":["str (raw text)","Document (with metadata)"],"output_types":["list[str] (text chunks)","list[Document] (chunks with metadata)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_6","uri":"capability://automation.workflow.callback.and.event.system.for.observability.and.tracing","name":"callback and event system for observability and tracing","description":"Implements a BaseCallbackHandler interface that enables hooking into Runnable execution at multiple points (on_llm_start, on_chain_start, on_tool_end, etc.). Callbacks integrate with LangSmith for production tracing and debugging. The system supports both synchronous and asynchronous callbacks, allowing custom logging, monitoring, and debugging without modifying chain code. Callbacks are registered via RunnableConfig and propagate through composed chains automatically.","intents":["I want to trace LLM calls and tool execution for debugging","I need to log chain execution metrics (latency, token usage) to monitoring systems","I want to implement custom debugging without modifying my chain code","I need to capture intermediate results for analysis and optimization"],"best_for":["Teams building production LLM applications","Developers debugging complex chains","Applications requiring observability and monitoring"],"limitations":["Callback overhead adds latency (~5-20ms per callback invocation depending on implementation)","Async callbacks can cause race conditions if not carefully implemented","Callbacks don't have access to full execution context — only event-specific data","No built-in sampling or filtering — all events trigger callbacks"],"requires":["Python 3.10+","langsmith>=0.3.45 for LangSmith integration","Custom callback implementation extending BaseCallbackHandler"],"input_types":["LLM input/output","tool arguments and results","chain start/end events"],"output_types":["logs (to stdout, files, or external systems)","metrics (latency, token counts)","traces (in LangSmith)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_7","uri":"capability://data.processing.analysis.structured.output.and.response.parsing.with.schema.validation","name":"structured output and response parsing with schema validation","description":"Provides OutputParser implementations (JsonOutputParser, PydanticOutputParser, XMLOutputParser) that convert LLM text responses into structured Python objects with validation. Parsers use Pydantic schemas to define expected output structure and validate responses before returning. The system includes retry logic to re-prompt the LLM if parsing fails, and supports both deterministic parsing (for models with structured output support) and heuristic parsing (for text-based responses).","intents":["I want to extract structured data from LLM responses with validation","I need to ensure LLM outputs conform to my application's data model","I want to automatically retry if the LLM produces invalid output","I need to support both JSON and other structured formats (XML, YAML)"],"best_for":["Developers building data extraction pipelines","Teams requiring guaranteed output structure","Applications using LLMs for structured reasoning"],"limitations":["Retry logic can cause exponential latency if the LLM consistently produces invalid output","Parsing errors require re-prompting, which increases token usage and latency","Some models (older GPT-3.5) struggle with structured output — requires careful prompt engineering","No built-in handling of partial or incomplete responses"],"requires":["Python 3.10+","pydantic>=2.7.4","Pydantic model defining expected output structure"],"input_types":["str (LLM response text)"],"output_types":["dict (parsed JSON)","BaseModel (Pydantic instance)","list (parsed array)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_8","uri":"capability://search.retrieval.retriever.abstraction.for.document.retrieval.and.rag.integration","name":"retriever abstraction for document retrieval and rag integration","description":"Defines BaseRetriever abstract class that normalizes document retrieval across different backends (vector stores, BM25, hybrid search, web search). Retrievers implement the Runnable interface, enabling composition with LLM chains for retrieval-augmented generation (RAG). The system supports both synchronous and asynchronous retrieval, with configurable search parameters (top_k, similarity threshold) and metadata filtering.","intents":["I want to build RAG applications that retrieve relevant documents before generating responses","I need to swap retrieval backends without changing my chain code","I want to combine multiple retrieval strategies (vector + keyword search)","I need to filter documents by metadata before returning results"],"best_for":["Teams building RAG systems","Developers implementing knowledge-grounded LLM applications","Applications requiring document-based reasoning"],"limitations":["Retrieval quality depends heavily on embedding model and chunking strategy — no automatic optimization","Metadata filtering requires backend support and may not be available across all retriever types","Combining multiple retrievers (hybrid search) requires manual orchestration","No built-in ranking or re-ranking of results"],"requires":["Python 3.10+","Vector store or search backend (Pinecone, Weaviate, Chroma, etc.)","Document embeddings (from OpenAI, Anthropic, or local model)"],"input_types":["str (query text)","dict (query with metadata filters)"],"output_types":["list[Document] (retrieved documents with metadata)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-core__cap_9","uri":"capability://planning.reasoning.agent.execution.framework.with.tool.use.and.planning","name":"agent execution framework with tool use and planning","description":"Provides agent abstractions (AgentExecutor, agent creation utilities) that implement agentic loops combining LLM reasoning with tool execution. Agents use ReAct-style prompting to generate thoughts and tool calls, then execute tools and feed results back to the LLM. The system integrates with LangGraph for more complex agent patterns and supports custom agent logic through middleware. Built-in error handling, max iteration limits, and timeout controls prevent runaway execution.","intents":["I want to build agents that reason about problems and use tools to solve them","I need to implement ReAct or similar agent patterns without writing orchestration code","I want to add custom middleware to agent execution (logging, filtering, validation)","I need to prevent agents from running indefinitely with iteration limits and timeouts"],"best_for":["Developers building autonomous agents","Teams implementing complex multi-step reasoning","Applications requiring tool-use orchestration"],"limitations":["Agent behavior is non-deterministic and depends on LLM reasoning quality","Debugging agent execution requires tracing through multiple LLM calls and tool invocations","Tool errors can cause agents to get stuck in loops — requires careful error handling","Token usage can be high due to repeated context passing and reasoning steps"],"requires":["Python 3.10+","LLM with function calling support","Tools defined as BaseTool or Runnable","Optional: langgraph>=1.0.2 for advanced agent patterns"],"input_types":["str (initial task or question)","dict (task with context)"],"output_types":["str (final answer)","dict (structured result with intermediate steps)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","pydantic>=2.7.4 for schema validation","langsmith>=0.3.45 for tracing and debugging","API keys for target LLM provider(s)","Provider-specific integration package (langchain-openai, langchain-anthropic, etc.)","Understanding of Runnable execution model","Async runtime (asyncio)","Provider support for batch or streaming APIs","langchain-core>=1.2.7","Provider-specific packages (langchain-openai, langchain-anthropic, etc.)"],"failure_modes":["Operator overloading syntax requires Python 3.10+ for full type hint support","Complex conditional branching requires explicit RunnableBranch or RunnableIf wrappers rather than native if/else","Debugging composed chains requires understanding LCEL compilation to execution graph","State management across chain steps requires explicit context passing via RunnableConfig","Provider-specific features (vision, function calling variants) require conditional logic or provider-specific subclasses","Token counting is approximate for some providers and requires model-specific implementations","Streaming behavior differs subtly between providers (chunk boundaries, error handling)","Rate limiting and retry logic must be configured per provider","Config propagation through deeply nested chains can be difficult to debug","Timeout enforcement depends on underlying implementation — not guaranteed across all Runnables","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-core","compare_url":"https://unfragile.ai/compare?artifact=pypi-langchain-core"}},"signature":"S2AyMM2V/2zze/yqFMcBfBa1zfi8sELy7d08NffEt/VNd507Eijxf6Yw5nLPnmyiTNDaNOKh4nahbPSTm5zBAA==","signedAt":"2026-06-20T08:24:58.469Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langchain-core","artifact":"https://unfragile.ai/pypi-langchain-core","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-langchain-core","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"}}