{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langchain-ai--langchain","slug":"langchain-ai--langchain","name":"langchain","type":"framework","url":"https://docs.langchain.com/langchain/","page_url":"https://unfragile.ai/langchain-ai--langchain","categories":["frameworks-sdks"],"tags":["agents","ai","ai-agents","anthropic","chatgpt","deepagents","enterprise","framework","gemini","generative-ai","langchain","langgraph","llm","multiagent","open-source","openai","pydantic","python","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langchain-ai--langchain__cap_0","uri":"capability://planning.reasoning.runnable.interface.based.component.composition.with.lcel","name":"runnable interface-based component composition with lcel","description":"LangChain provides a unified Runnable abstraction that enables declarative chaining of LLM calls, tools, retrievers, and custom components through LangChain Expression Language (LCEL). Components implement invoke(), stream(), batch(), and async variants, allowing developers to compose complex workflows with pipe operators while maintaining type safety through Pydantic validation. The architecture supports automatic parallelization, fallback chains, and conditional routing without requiring explicit orchestration code.","intents":["I want to chain multiple LLM calls and tools together without writing boilerplate orchestration code","I need to build reusable, composable components that work with any LLM provider","I want to support streaming, batching, and async execution without rewriting my pipeline logic","I need type-safe component composition with runtime validation"],"best_for":["Teams building modular LLM applications with multiple provider integrations","Developers who want to avoid vendor lock-in through abstraction layers","Engineers building production systems requiring streaming and batch processing"],"limitations":["LCEL syntax requires learning a new composition paradigm — not compatible with imperative Python patterns","Abstraction overhead adds ~50-100ms per chain step due to Pydantic validation and interface dispatch","Debugging complex LCEL chains can be opaque — stack traces don't map clearly to original composition code","Type hints in LCEL chains are best-effort; runtime type mismatches only surface at execution time"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4"],"input_types":["Python objects (dicts, BaseMessage, custom Pydantic models)","Strings","Structured data"],"output_types":["Python objects","Strings","Streaming iterables","Async generators"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--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 over language models from OpenAI, Anthropic, Groq, Fireworks, Ollama, and others through a unified BaseLanguageModel interface. Each provider integration handles authentication, request formatting, response parsing, and streaming via provider-specific SDKs while exposing identical invoke/stream/batch methods. The core layer manages message serialization (BaseMessage types), token counting, and fallback logic, allowing applications to swap providers without code changes.","intents":["I want to support multiple LLM providers without rewriting my application logic","I need to switch between OpenAI, Anthropic, and local models (Ollama) based on cost or latency requirements","I want unified error handling and retry logic across all provider APIs","I need to count tokens consistently across different model families"],"best_for":["Teams building cost-optimized systems that need to route between expensive and cheap models","Enterprises requiring multi-vendor strategies to avoid single-provider dependency","Developers prototyping with cloud models but deploying with local Ollama instances"],"limitations":["Provider-specific features (vision, function calling schemas, streaming options) require conditional code — abstraction doesn't hide all differences","Token counting is approximate for non-OpenAI models; Anthropic and Groq use different tokenization schemes","Streaming behavior varies by provider — some buffer responses, others stream token-by-token","Rate limiting and quota management are provider-specific; LangChain doesn't provide unified rate limiting"],"requires":["Python 3.10+","langchain-core>=1.2.7","Provider-specific API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Provider SDK (openai>=1.0, anthropic>=0.7, etc.)"],"input_types":["BaseMessage lists (system, human, assistant, tool messages)","Strings (auto-converted to HumanMessage)","Structured prompts with variables"],"output_types":["AIMessage (text + optional tool calls)","Streaming token iterables","Structured outputs (via JSON mode or function calling)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_10","uri":"capability://memory.knowledge.embedding.model.abstraction.with.vector.store.integration","name":"embedding model abstraction with vector store integration","description":"LangChain provides a Embeddings interface that abstracts over embedding models (OpenAI, Hugging Face, local models) and integrates with vector stores (Pinecone, Weaviate, FAISS, Chroma, etc.). The framework handles embedding batching, caching, and async execution, and provides a unified interface for indexing documents and querying vectors. Vector store integrations handle storage, retrieval, and filtering, enabling semantic search without provider-specific code.","intents":["I want to embed documents and queries using different embedding models without rewriting code","I need to index documents in a vector store and retrieve similar documents by semantic similarity","I want to support multiple vector stores (Pinecone, FAISS, Chroma) without duplicating integration code","I need to batch embed documents efficiently and cache embeddings to reduce API calls"],"best_for":["Teams building semantic search and RAG systems","Developers experimenting with different embedding models and vector stores","Applications requiring efficient document indexing and retrieval at scale"],"limitations":["Embedding quality depends entirely on the model — LangChain doesn't provide guidance on model selection or fine-tuning","Vector store integrations are thin adapters — each store has different query syntax, filtering, and performance characteristics","Embedding caching is optional and requires external storage — no built-in cache","Batch embedding is limited by model rate limits and context windows — no automatic batching optimization","Vector store metadata filtering is inconsistent across providers — some support complex filters, others don't"],"requires":["Python 3.10+","langchain-core>=1.2.7","Embedding model (OpenAI, Hugging Face, local)","Vector store (Pinecone, Weaviate, FAISS, Chroma, etc.)"],"input_types":["Text strings (for embedding)","Documents (with metadata)","Query strings"],"output_types":["Vector embeddings (lists of floats)","Retrieved documents (ranked by similarity)","Similarity scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_11","uri":"capability://automation.workflow.configuration.and.runtime.control.via.environment.variables.and.settings","name":"configuration and runtime control via environment variables and settings","description":"LangChain uses Pydantic Settings to manage configuration (API keys, model names, endpoints, feature flags) via environment variables, .env files, and programmatic overrides. This enables environment-specific configuration without code changes, and integrates with deployment platforms (Docker, Kubernetes, serverless). The framework also provides runtime control via context managers and configuration objects, allowing fine-grained control over component behavior (timeouts, retries, streaming options).","intents":["I want to configure API keys and model names via environment variables for different deployment environments","I need to override default settings (timeouts, retries, streaming) at runtime without modifying code","I want to use different models in development (local Ollama) vs. production (OpenAI)","I need to manage secrets securely without hardcoding them in application code"],"best_for":["Teams deploying LLM applications across multiple environments (dev, staging, production)","Developers using containerized deployments (Docker, Kubernetes) requiring environment-based configuration","Applications requiring runtime configuration changes without redeployment"],"limitations":["Configuration is flat — no hierarchical or nested configuration support","Environment variable names are verbose and easy to mistype — no validation at startup","Runtime configuration changes don't affect already-instantiated components — requires careful lifecycle management","No built-in configuration versioning or rollback — configuration changes are immediate and global"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4"],"input_types":["Environment variables",".env files","Configuration objects (dicts, Pydantic models)"],"output_types":["Configured components (LLMs, tools, retrievers, etc.)","Runtime settings (timeouts, retries, streaming options)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_12","uri":"capability://automation.workflow.testing.framework.and.vcr.based.test.recording.for.reproducibility","name":"testing framework and vcr-based test recording for reproducibility","description":"LangChain provides a standard testing framework (pytest-based) with VCR (Video Cassette Recorder) integration for recording and replaying HTTP interactions. This enables tests to run without external API calls, reducing flakiness and cost. The framework includes fixtures for common test scenarios (mock LLMs, in-memory vector stores, etc.) and supports both unit tests (component-level) and integration tests (end-to-end workflows).","intents":["I want to test LLM components without making real API calls","I need to record API interactions once and replay them in tests for reproducibility","I want to test agent behavior with deterministic LLM responses","I need to ensure my tests run quickly and cheaply without external dependencies"],"best_for":["Teams building LLM applications with comprehensive test coverage","Developers who want to test agent behavior without external API costs","CI/CD pipelines requiring fast, reliable tests without external dependencies"],"limitations":["VCR recordings are brittle — API response format changes break recorded interactions","Recorded responses don't capture all edge cases — tests may miss real-world failures","VCR setup requires careful configuration — recording mode, filtering, matching rules must be tuned","Mock LLMs are simplistic — they don't capture real LLM behavior (hallucination, token limits, etc.)"],"requires":["Python 3.10+","pytest","vcrpy (for VCR integration)","langchain-core>=1.2.7"],"input_types":["Test code (pytest-based)","Recorded HTTP interactions (YAML files)"],"output_types":["Test results (pass/fail)","Recorded interactions (for replay)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_2","uri":"capability://tool.use.integration.schema.based.tool.function.calling.with.multi.provider.support","name":"schema-based tool/function calling with multi-provider support","description":"LangChain provides a BaseTool abstraction that converts Python functions into tool schemas compatible with OpenAI, Anthropic, and Groq function-calling APIs. Tools are defined via Pydantic models for input validation, and the framework automatically generates JSON schemas, handles tool invocation, and manages tool-use message types. The agent system can bind tools to models and execute them in agentic loops, with built-in support for parallel tool calling and error recovery.","intents":["I want to give LLMs access to external APIs and Python functions without writing provider-specific schemas","I need automatic input validation and type coercion for tool arguments","I want to build agents that can call multiple tools in parallel and handle failures gracefully","I need to support both OpenAI function calling and Anthropic tool_use without duplicating tool definitions"],"best_for":["Teams building AI agents with external tool access (APIs, databases, file systems)","Developers who want to avoid manual JSON schema generation for function calling","Multi-provider applications requiring tool definitions to work across OpenAI, Anthropic, and Groq"],"limitations":["Tool schemas are generated from Pydantic models — complex nested types may not translate cleanly to JSON schema","Tool execution errors don't automatically retry; agents must implement retry logic in their loop","Parallel tool calling is supported by some providers (OpenAI, Anthropic) but not others — requires conditional handling","Tool descriptions are free-form text; no structured metadata for categorization or discovery"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 for tool input schemas","LLM provider with function calling support (OpenAI, Anthropic, Groq)"],"input_types":["Python functions with type hints","Pydantic models (for input validation)","Tool descriptions (strings)"],"output_types":["JSON schemas (for provider APIs)","Tool execution results (any Python type)","ToolMessage (for agent message history)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_3","uri":"capability://planning.reasoning.agentic.loop.orchestration.with.middleware.and.state.management","name":"agentic loop orchestration with middleware and state management","description":"LangChain integrates with LangGraph to provide agentic loop orchestration, where agents iteratively call LLMs, execute tools, and update state based on results. The middleware architecture allows custom logic to intercept and modify agent behavior at each step (pre-tool-call, post-tool-call, etc.). State is managed as a dictionary that persists across loop iterations, enabling agents to maintain context, track tool calls, and implement complex decision logic without explicit state machine code.","intents":["I want to build agents that loop until a goal is achieved, calling tools and updating state dynamically","I need to inject custom logic into agent execution (logging, validation, cost tracking) without modifying core agent code","I want agents to maintain persistent state across multiple tool calls and LLM invocations","I need to implement complex agent behaviors like tool selection strategies, retry logic, and early termination"],"best_for":["Teams building multi-step AI agents (research agents, code generation agents, planning agents)","Developers who need fine-grained control over agent execution flow and state transitions","Production systems requiring observability and custom middleware for cost/latency tracking"],"limitations":["Agentic loops can be unpredictable — LLM tool selection is non-deterministic, making debugging difficult","State management is manual — developers must explicitly update state dictionaries; no automatic conflict resolution","Loop termination requires explicit stopping conditions; runaway loops can consume unbounded tokens and API quota","Middleware ordering matters — interceptors are applied in registration order, and conflicts aren't detected automatically"],"requires":["Python 3.10+","langchain-core>=1.2.7","langgraph>=1.0.2 (for graph-based agent orchestration)","LLM with function calling support"],"input_types":["Initial state (dict with user input, context, etc.)","Tool definitions (BaseTool instances)","Agent configuration (model, tools, system prompt)"],"output_types":["Final state (dict with agent output, tool call history, etc.)","Streaming state updates (for real-time UI updates)","Structured agent output (via response formatting)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_4","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.pipeline.assembly","name":"retrieval-augmented generation (rag) pipeline assembly","description":"LangChain provides abstractions for building RAG pipelines: document loaders ingest data from files/APIs, text splitters chunk documents, embeddings convert text to vectors, vector stores index and retrieve relevant documents, and retrievers fetch context for LLM prompts. These components compose via the Runnable interface, allowing developers to build end-to-end RAG systems by connecting loaders → splitters → embeddings → vector stores → retrievers → LLM chains without writing custom integration code.","intents":["I want to build a Q&A system over my documents without writing custom indexing and retrieval logic","I need to support multiple document formats (PDFs, CSVs, web pages) with a unified pipeline","I want to experiment with different chunking strategies and embedding models without rewriting my pipeline","I need to retrieve relevant context from a large document corpus and feed it to an LLM"],"best_for":["Teams building document-based Q&A systems (customer support, knowledge base search)","Developers prototyping RAG applications and experimenting with retrieval strategies","Enterprises with large document corpora requiring semantic search capabilities"],"limitations":["Vector store integrations are thin adapters — each store has different query syntax, filtering capabilities, and performance characteristics","Chunking strategies are heuristic-based (recursive character split, token-based); no automatic optimization for domain-specific documents","Embedding quality depends entirely on the embedding model — LangChain doesn't provide guidance on model selection","Retrieval is stateless — no built-in re-ranking, diversity sampling, or context-aware retrieval strategies","No built-in handling of document updates — requires manual re-indexing when source documents change"],"requires":["Python 3.10+","langchain-core>=1.2.7","langchain-text-splitters>=1.1.0","Embedding model (OpenAI, Hugging Face, local)","Vector store (Pinecone, Weaviate, FAISS, Chroma, etc.)"],"input_types":["Documents (text, PDF, CSV, web pages)","Query strings","Structured metadata (for filtering)"],"output_types":["Chunked documents (with metadata)","Vector embeddings","Retrieved document chunks (ranked by relevance)","LLM responses augmented with retrieved context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_5","uri":"capability://text.generation.language.prompt.template.management.with.variable.substitution.and.formatting","name":"prompt template management with variable substitution and formatting","description":"LangChain provides PromptTemplate and ChatPromptTemplate classes that define prompt structures with named variables, input validation via Pydantic, and automatic formatting. Templates support partial variable binding (freezing some variables while leaving others open), composition with other templates, and output formatting (string, BaseMessage list, etc.). This enables reusable, testable prompt definitions that decouple prompt logic from application code.","intents":["I want to define reusable prompt templates with named variables instead of string concatenation","I need to validate prompt inputs before sending to the LLM (e.g., ensure required fields are present)","I want to compose multiple prompts together (system prompt + user prompt + few-shot examples)","I need to test prompts independently of LLM calls"],"best_for":["Teams managing multiple prompts across different use cases (classification, summarization, Q&A)","Developers who want to version control and test prompts separately from application code","Applications requiring dynamic prompt construction based on runtime context"],"limitations":["Template syntax is basic — no conditional logic, loops, or complex formatting (use Jinja2 for advanced cases)","Variable validation is optional — developers must explicitly define input schemas via Pydantic","No built-in prompt versioning or A/B testing framework","Template composition is manual — no automatic merging of input schemas when combining templates"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 (for input validation)"],"input_types":["Variable dictionaries (for formatting)","Pydantic models (for validated input)","Strings (for partial binding)"],"output_types":["Formatted strings","BaseMessage lists (for chat models)","Prompt objects (for composition)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_6","uri":"capability://data.processing.analysis.document.text.splitting.with.configurable.chunking.strategies","name":"document text splitting with configurable chunking strategies","description":"LangChain's text splitters (RecursiveCharacterTextSplitter, TokenTextSplitter, etc.) chunk documents into overlapping segments while preserving semantic boundaries. Splitters support configurable chunk size, overlap, and separator hierarchies (paragraphs → sentences → characters). The langchain-text-splitters package provides language-specific splitters (Python, Markdown, etc.) that respect code structure, and custom splitters can be implemented via the BaseSplitter interface.","intents":["I want to chunk documents into fixed-size pieces for embedding and retrieval without losing context","I need to preserve semantic boundaries (paragraphs, code blocks) when splitting documents","I want to use token-based chunking to respect LLM context windows","I need language-specific splitting (e.g., split Python code at function boundaries, not arbitrary character positions)"],"best_for":["Teams building RAG systems that need to chunk documents before embedding","Developers working with long documents (books, research papers, code repositories)","Applications requiring semantic-aware chunking to preserve document structure"],"limitations":["Chunking is heuristic-based — no automatic optimization for document type or domain","Token-based splitting requires a tokenizer (OpenAI, Hugging Face); adds latency and dependency","Overlap between chunks increases storage and retrieval cost — no automatic optimization","Language-specific splitters are limited to a few languages (Python, Markdown, LaTeX); custom languages require custom splitters","No built-in handling of metadata preservation — chunk boundaries may split structured data"],"requires":["Python 3.10+","langchain-text-splitters>=1.1.0","Tokenizer (for token-based splitting): tiktoken or transformers"],"input_types":["Text strings","Documents (with metadata)"],"output_types":["List of text chunks","List of Document objects (with preserved metadata)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_7","uri":"capability://automation.workflow.callback.and.event.system.for.observability.and.instrumentation","name":"callback and event system for observability and instrumentation","description":"LangChain's callback system allows developers to hook into component execution at multiple points (start, end, error) via BaseCallbackHandler implementations. Callbacks receive detailed event data (component name, inputs, outputs, latency, tokens used) and can implement custom logic (logging, metrics collection, tracing). The system integrates with LangSmith for production observability, and callbacks can be registered globally or per-component, enabling fine-grained instrumentation without modifying component code.","intents":["I want to log and trace LLM calls, tool executions, and agent steps for debugging","I need to collect metrics (latency, token usage, cost) across my LLM application","I want to integrate with observability platforms (LangSmith, Datadog, custom logging) without modifying application code","I need to implement custom behavior on component success/failure (e.g., alert on errors, cache successful calls)"],"best_for":["Teams running LLM applications in production and requiring observability","Developers debugging complex agent behaviors and needing detailed execution traces","Applications requiring cost tracking and token usage monitoring"],"limitations":["Callback overhead adds latency — each component invocation triggers callback dispatch (~5-10ms per callback)","Callbacks are fire-and-forget — errors in callbacks don't propagate to component execution","No built-in filtering or sampling — all events are processed, which can be expensive at scale","Callback data is component-specific — no standardized event schema across all component types","Async callbacks can cause race conditions if not carefully implemented"],"requires":["Python 3.10+","langchain-core>=1.2.7","Optional: langsmith>=0.3.45 (for LangSmith integration)"],"input_types":["Component execution events (start, end, error)","Detailed event data (inputs, outputs, latency, tokens)"],"output_types":["Logs (to stdout, files, or external systems)","Metrics (latency, token usage, cost)","Traces (for distributed tracing systems)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_8","uri":"capability://text.generation.language.structured.output.formatting.with.json.schema.and.validation","name":"structured output formatting with json schema and validation","description":"LangChain enables structured outputs from LLMs by binding Pydantic models to language models, which automatically generates JSON schemas and parses LLM responses into validated Python objects. The framework supports both JSON mode (for models that support it) and function calling-based parsing, with automatic retry logic when parsing fails. This enables type-safe LLM outputs without manual JSON parsing or validation code.","intents":["I want LLM outputs to be structured (e.g., JSON) instead of free-form text","I need to validate LLM outputs against a schema before using them in my application","I want automatic retry logic when the LLM produces invalid JSON or doesn't follow the schema","I need to extract specific fields from LLM responses without manual parsing"],"best_for":["Teams building applications that depend on structured LLM outputs (classification, extraction, code generation)","Developers who want type safety and validation for LLM responses","Applications requiring reliable structured outputs with automatic error recovery"],"limitations":["Not all models support JSON mode or function calling — fallback to text parsing is less reliable","Retry logic can increase latency and token usage — failed parses consume tokens without progress","Complex nested schemas may not translate cleanly to JSON schema — some Pydantic features aren't supported","LLM hallucination can still produce invalid JSON or values that don't match the schema — validation catches errors but doesn't prevent them","Schema size affects token usage — large schemas consume more tokens in the prompt"],"requires":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4 (for schema definition)","LLM with JSON mode or function calling support (OpenAI, Anthropic, Groq)"],"input_types":["Pydantic models (for schema definition)","LLM responses (text or function calls)"],"output_types":["Validated Python objects (instances of Pydantic models)","Structured data (dicts, lists, primitives)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langchain__cap_9","uri":"capability://text.generation.language.message.type.abstraction.for.multi.turn.conversations","name":"message type abstraction for multi-turn conversations","description":"LangChain defines a BaseMessage hierarchy (HumanMessage, AIMessage, SystemMessage, ToolMessage, etc.) that standardizes conversation history across all LLM providers. Messages include role, content (text or multi-modal), and optional metadata (tool calls, tool results, etc.). This abstraction enables consistent message handling across OpenAI, Anthropic, and other providers, and allows agents to maintain conversation history without provider-specific formatting.","intents":["I want to maintain conversation history in a provider-agnostic format","I need to handle tool calls and tool results in multi-turn conversations","I want to support multi-modal messages (text + images) across different providers","I need to serialize and deserialize conversation history for persistence"],"best_for":["Teams building multi-turn conversational agents","Applications requiring conversation history persistence and replay","Multi-provider applications needing consistent message formatting"],"limitations":["Message serialization is provider-specific — some providers don't support all message types","Tool message handling varies by provider — OpenAI and Anthropic have different tool result formats","Multi-modal content support is limited — not all providers support images, audio, or video","Message history grows unbounded — no built-in truncation or summarization for long conversations"],"requires":["Python 3.10+","langchain-core>=1.2.7"],"input_types":["Strings (auto-converted to HumanMessage)","Message objects (HumanMessage, AIMessage, etc.)","Message lists (conversation history)"],"output_types":["Message objects","Message lists","Serialized messages (JSON, dicts)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","langchain-core>=1.2.7","Pydantic>=2.7.4","Provider-specific API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Provider SDK (openai>=1.0, anthropic>=0.7, etc.)","Embedding model (OpenAI, Hugging Face, local)","Vector store (Pinecone, Weaviate, FAISS, Chroma, etc.)","pytest","vcrpy (for VCR integration)","Pydantic>=2.7.4 for tool input schemas"],"failure_modes":["LCEL syntax requires learning a new composition paradigm — not compatible with imperative Python patterns","Abstraction overhead adds ~50-100ms per chain step due to Pydantic validation and interface dispatch","Debugging complex LCEL chains can be opaque — stack traces don't map clearly to original composition code","Type hints in LCEL chains are best-effort; runtime type mismatches only surface at execution time","Provider-specific features (vision, function calling schemas, streaming options) require conditional code — abstraction doesn't hide all differences","Token counting is approximate for non-OpenAI models; Anthropic and Groq use different tokenization schemes","Streaming behavior varies by provider — some buffer responses, others stream token-by-token","Rate limiting and quota management are provider-specific; LangChain doesn't provide unified rate limiting","Embedding quality depends entirely on the model — LangChain doesn't provide guidance on model selection or fine-tuning","Vector store integrations are thin adapters — each store has different query syntax, filtering, and performance characteristics","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9464569256562354,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T12:07:39Z"},"community":{"stars":135673,"forks":22428,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain-ai--langchain","compare_url":"https://unfragile.ai/compare?artifact=langchain-ai--langchain"}},"signature":"TgG4ybT3yAMK4CX+w448dzqMYFElzPjqkwJ+yOF7gROivZbyzXVeqtJpdo6vYNDn4+EfHGXYvgU5Lhou6yKzCA==","signedAt":"2026-06-19T19:11:33.571Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-ai--langchain","artifact":"https://unfragile.ai/langchain-ai--langchain","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-ai--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"}}