{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langchain-openai","slug":"pypi-langchain-openai","name":"langchain-openai","type":"framework","url":"https://pypi.org/project/langchain-openai/","page_url":"https://unfragile.ai/pypi-langchain-openai","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langchain-openai__cap_0","uri":"capability://tool.use.integration.openai.chat.model.integration.via.runnable.interface","name":"openai chat model integration via runnable interface","description":"Wraps OpenAI's chat completion API (gpt-4, gpt-3.5-turbo, etc.) as a LangChain Runnable, enabling standardized invocation through the LCEL (LangChain Expression Language) abstraction. Implements streaming, batch processing, and async execution patterns through the Runnable protocol, with automatic token counting via tiktoken and structured output parsing via Pydantic models. Handles message formatting, tool/function calling schemas, and response streaming with built-in retry logic via tenacity.","intents":["I want to use OpenAI models in a LangChain chain without writing provider-specific code","I need to stream responses from OpenAI while maintaining compatibility with other LangChain components","I want to call OpenAI functions/tools through a standardized interface that works with agents"],"best_for":["Teams building multi-provider LLM applications who want provider abstraction","Developers migrating from direct OpenAI SDK to LangChain's composable architecture","Builders prototyping agents that may swap providers without refactoring"],"limitations":["Adds ~50-100ms overhead per call due to Runnable abstraction layer and message serialization","No built-in caching of OpenAI responses — requires external integration with LangSmith or Redis","Structured output (JSON mode) requires manual schema definition; no automatic Pydantic-to-OpenAI schema conversion","Vision capabilities limited to what OpenAI API supports; no local image preprocessing"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0","pydantic >=2.7.4","OPENAI_API_KEY environment variable or explicit api_key parameter"],"input_types":["BaseMessage objects (HumanMessage, SystemMessage, AIMessage, ToolMessage)","Plain strings (auto-wrapped as HumanMessage)","Message lists with role/content dicts"],"output_types":["AIMessage with content string","AIMessage with tool_calls list (for function calling)","Streaming iterables of AIMessageChunk","Structured Pydantic models (via with_structured_output)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_1","uri":"capability://data.processing.analysis.openai.embedding.model.integration.with.vector.store.compatibility","name":"openai embedding model integration with vector store compatibility","description":"Wraps OpenAI's embedding API (text-embedding-3-small, text-embedding-3-large, ada) as a LangChain Embeddings class, enabling standardized embedding generation with batch processing, async support, and automatic dimension handling. Integrates seamlessly with LangChain's vector store ecosystem (Pinecone, Weaviate, FAISS, etc.) through the Embeddings interface, supporting both embed_query (single) and embed_documents (batch) methods with configurable chunk size and retry logic.","intents":["I want to generate embeddings from OpenAI and store them in a vector database without writing adapter code","I need to batch embed large document collections efficiently using OpenAI's API","I want to swap embedding providers (OpenAI → Cohere → local) without changing vector store code"],"best_for":["RAG pipeline builders using OpenAI embeddings with LangChain vector stores","Teams building semantic search systems that need provider abstraction","Developers migrating from direct OpenAI embedding calls to LangChain's standardized interface"],"limitations":["No local caching of embeddings — each unique text requires an API call unless wrapped with external cache","Batch size limited by OpenAI API (max 2048 texts per request); larger batches require manual chunking","Dimension reduction not supported — must use OpenAI's native dimensions (1536 for ada, 3072 for 3-large)","No built-in similarity search — requires separate vector store for retrieval"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0","OPENAI_API_KEY environment variable"],"input_types":["Single string (embed_query)","List of strings (embed_documents)","Plain text or preprocessed document chunks"],"output_types":["List of float lists (embeddings as vectors)","Numpy arrays or torch tensors (via downstream processing)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_10","uri":"capability://data.processing.analysis.pydantic.based.structured.output.with.json.schema.validation","name":"pydantic-based structured output with json schema validation","description":"Enables structured output from OpenAI using with_structured_output() method that binds a Pydantic model to the chat model, automatically converting model schema to OpenAI's JSON mode format. Parses OpenAI's JSON responses back into validated Pydantic instances, ensuring type safety and field validation without manual JSON parsing. Supports both OpenAI's native JSON mode and fallback parsing for models without native support.","intents":["I want OpenAI to return structured data (JSON) that automatically validates against my Pydantic model","I need to extract specific fields from LLM responses with type safety","I want to avoid manual JSON parsing and validation in my application code"],"best_for":["Developers building data extraction pipelines with LLMs","Teams implementing structured output requirements (APIs, databases)","Builders creating type-safe LLM applications with Pydantic"],"limitations":["JSON mode requires explicit schema definition; complex nested models may exceed OpenAI's schema complexity limits","Fallback parsing adds latency and may fail on malformed JSON; native JSON mode is more reliable","No automatic schema optimization — overly complex schemas may reduce LLM accuracy","Validation errors don't trigger retries; invalid responses fail immediately"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0 with JSON mode support","pydantic >=2.7.4","Model supporting JSON mode (gpt-4-turbo, gpt-3.5-turbo-1106+)"],"input_types":["Pydantic model class","Prompt with instructions for structured output"],"output_types":["Validated Pydantic model instance","Structured data with type safety","Validation errors on schema mismatch"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_11","uri":"capability://image.visual.vision.model.support.with.image.input.handling","name":"vision model support with image input handling","description":"Extends ChatOpenAI to support OpenAI's vision models (gpt-4-vision, gpt-4-turbo) with automatic image input handling through HumanMessage with image_url or base64 content. Supports multiple image formats (JPEG, PNG, GIF, WebP) and handles image preprocessing (resizing, encoding) transparently. Integrates with LangChain's document loaders to enable image analysis in document processing pipelines.","intents":["I want to analyze images using OpenAI's vision models in a LangChain chain","I need to process documents with embedded images and extract text/data from them","I want to build multi-modal applications that mix text and image understanding"],"best_for":["Developers building document analysis applications with images","Teams implementing visual question answering systems","Builders creating multi-modal RAG pipelines with image understanding"],"limitations":["Vision models are significantly more expensive than text models; cost per image can be 10-20x higher","Image preprocessing (resizing, format conversion) requires additional libraries; no built-in image optimization","Vision model latency is higher (~2-5s per image); not suitable for real-time applications","No built-in image caching — identical images require separate API calls"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0 with vision support","Access to gpt-4-vision or gpt-4-turbo models","Image preprocessing libraries (PIL, opencv) for format handling"],"input_types":["Image URLs (http/https)","Base64-encoded image data","Local file paths (converted to base64)","Mixed text and image content in HumanMessage"],"output_types":["Text descriptions of images","Extracted data from images","Structured analysis (via with_structured_output)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_12","uri":"capability://automation.workflow.batch.processing.api.integration.for.cost.optimization","name":"batch processing api integration for cost optimization","description":"Integrates with OpenAI's Batch API to enable cost-optimized processing of large numbers of requests with 50% discount, trading latency for savings. Automatically batches multiple LLM calls into a single batch job, handles job submission and result retrieval, and integrates with LangChain's batch execution patterns. Suitable for non-time-sensitive workloads like data processing, analysis, and evaluation.","intents":["I want to process thousands of documents with LLMs at 50% cost reduction","I need to run large-scale evaluations or benchmarks without budget constraints","I want to batch similar requests together for efficiency without manual job management"],"best_for":["Teams processing large datasets with LLMs on a budget","Builders implementing evaluation and benchmarking pipelines","Developers optimizing cost for non-time-sensitive LLM workloads"],"limitations":["Batch API has 24-hour processing window; not suitable for real-time or time-sensitive applications","Batch job management requires polling or webhook handling; no built-in job status monitoring","Minimum batch size and cost thresholds may not justify batching for small workloads","Failed requests in batch require manual retry; no automatic error recovery"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0 with batch API support","OPENAI_API_KEY with batch API access"],"input_types":["List of LLM requests (prompts, messages)","Batch configuration (timeout, priority)"],"output_types":["Batch job ID","Batch results (after processing)","Cost savings metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_2","uri":"capability://tool.use.integration.tool.function.calling.schema.binding.with.structured.output.parsing","name":"tool/function calling schema binding with structured output parsing","description":"Binds OpenAI's function calling API to LangChain tools through a schema-based registry that converts BaseTool objects to OpenAI function definitions and parses tool_calls from responses back into ToolMessage objects. Supports both legacy 'functions' parameter and modern 'tools' parameter with automatic schema generation from Pydantic models, enabling agents to invoke external tools with type-safe argument validation. Handles parallel tool calling, tool error recovery, and integration with LangChain's agent loop.","intents":["I want my LLM agent to call external tools (APIs, databases, calculators) with type-safe arguments","I need to convert my Python functions into OpenAI-compatible tool schemas without manual JSON definition","I want structured output from OpenAI that maps directly to my Pydantic models"],"best_for":["Agentic application builders using LangChain agents with OpenAI","Teams building tool-using LLMs that require argument validation","Developers implementing multi-step workflows where LLMs decide which tools to call"],"limitations":["Schema generation from Pydantic models requires explicit field descriptions — missing descriptions reduce LLM accuracy","No automatic tool result formatting — developers must manually create ToolMessage objects with tool output","Parallel tool calling supported by OpenAI but requires explicit handling in agent loop; no built-in orchestration","Tool calling failures (invalid arguments, missing required fields) require custom error handling in agent middleware"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0 (with tools parameter support)","pydantic >=2.7.4 for schema generation","BaseTool or Runnable with tool_choice='auto' or explicit tool selection"],"input_types":["BaseTool objects with name, description, args_schema","Pydantic models (for structured output)","Tool lists passed to bind_tools() method"],"output_types":["AIMessage with tool_calls list containing ToolCall objects","ToolMessage objects (created by agent) with tool_input and tool_output","Structured Pydantic model instances (via with_structured_output)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_3","uri":"capability://automation.workflow.async.and.streaming.response.handling.with.backpressure.support","name":"async and streaming response handling with backpressure support","description":"Implements async/await patterns and streaming iterators for OpenAI responses through the Runnable protocol, enabling non-blocking LLM calls and token-by-token output consumption. Supports ainvoke() for async single calls, astream() for async token streaming, and abatch() for concurrent batch processing with configurable concurrency limits. Handles backpressure via async generators and integrates with LangChain's callback system for real-time event tracking (on_llm_start, on_llm_stream, on_llm_end).","intents":["I want to stream LLM responses to users in real-time without blocking the application","I need to process multiple OpenAI requests concurrently while respecting rate limits","I want to track LLM execution events (start, token generation, completion) for logging and monitoring"],"best_for":["Web application developers building real-time chat interfaces with streaming responses","Data pipeline builders processing large document collections with concurrent LLM calls","Teams implementing observability and monitoring for LLM applications"],"limitations":["Streaming adds ~20-50ms latency per token due to network round-trips; not suitable for ultra-low-latency applications","Backpressure handling requires explicit async context management; mixing sync and async code can cause deadlocks","Callback system adds ~5-10ms overhead per event; high-frequency callbacks (every token) may impact throughput","No built-in retry logic for streaming failures — partial responses may be lost if connection drops mid-stream"],"requires":["Python >=3.10 with asyncio support","langchain-core >=1.2.7","openai >=1.0.0 with streaming support","Async-compatible event loop (uvicorn, asyncio, etc.)"],"input_types":["BaseMessage or string input","Async iterables of inputs (for abatch)","Callback handlers (for event tracking)"],"output_types":["Async iterator of AIMessageChunk (for astream)","Single AIMessage (for ainvoke)","List of AIMessage (for abatch)","Event callbacks (on_llm_start, on_llm_stream, on_llm_end)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_4","uri":"capability://safety.moderation.automatic.retry.and.error.handling.with.exponential.backoff","name":"automatic retry and error handling with exponential backoff","description":"Wraps OpenAI API calls with tenacity-based retry logic that automatically handles rate limits (429), server errors (5xx), and transient failures with exponential backoff and jitter. Configurable retry attempts, wait strategies, and stop conditions enable graceful degradation without explicit error handling in application code. Integrates with LangChain's callback system to emit retry events for observability.","intents":["I want my LLM application to automatically retry on rate limits without crashing","I need to handle transient OpenAI API failures gracefully without manual try/catch blocks","I want visibility into retry attempts for debugging and monitoring"],"best_for":["Production LLM applications requiring high availability and fault tolerance","Batch processing pipelines that need to handle API rate limiting","Teams building resilient agents that operate in unreliable network conditions"],"limitations":["Exponential backoff can cause significant latency (up to minutes) for heavily rate-limited scenarios","Retry logic only handles transient failures; permanent errors (invalid API key, model not found) still fail immediately","No built-in circuit breaker — repeated failures don't prevent subsequent requests, risking cascading failures","Retry state is not persisted — application restart loses retry history and may cause duplicate API calls"],"requires":["Python >=3.10","langchain-core >=1.2.7","tenacity >=8.1.0","openai >=1.0.0"],"input_types":["Any OpenAI API call (chat completion, embedding, etc.)"],"output_types":["Successful response after retries","Exception after max retries exceeded","Callback events (on_retry)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_5","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.for.openai.models","name":"token counting and cost estimation for openai models","description":"Provides token counting via tiktoken library for OpenAI models (gpt-4, gpt-3.5-turbo, etc.), enabling accurate cost estimation and context window management before API calls. Implements get_num_tokens() method that counts tokens in prompts and messages, and integrates with LangChain's token counter callbacks to track cumulative token usage across chains. Supports both encoding-based counting (fast, local) and API-based counting (accurate for edge cases).","intents":["I want to estimate API costs before making OpenAI calls to avoid budget overruns","I need to check if my prompt fits within the model's context window before sending it","I want to track total token usage across my LLM application for billing and optimization"],"best_for":["Cost-conscious teams building LLM applications with budget constraints","Developers optimizing prompts and context to minimize token usage","Teams implementing token-based rate limiting or quota management"],"limitations":["Tiktoken encoding may differ slightly from OpenAI's server-side counting (~1-2% variance) due to special token handling","Token counting is synchronous and blocks execution; high-frequency counting can add latency","No built-in cost tracking across multiple models or providers — requires manual aggregation","Context window limits are static; doesn't account for dynamic context from retrieval or tool outputs"],"requires":["Python >=3.10","langchain-core >=1.2.7","tiktoken >=0.5.0","openai >=1.0.0"],"input_types":["String prompts","BaseMessage objects","Message lists"],"output_types":["Integer token count","Cost estimate (tokens × price per token)","Callback events with token usage metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_6","uri":"capability://text.generation.language.multi.model.support.with.dynamic.model.selection","name":"multi-model support with dynamic model selection","description":"Supports multiple OpenAI model families (gpt-4, gpt-4-turbo, gpt-3.5-turbo, gpt-4-vision, etc.) through a single ChatOpenAI class with model parameter, enabling runtime model switching without code changes. Automatically adapts behavior based on model capabilities (vision support, function calling, JSON mode, etc.) and handles model-specific parameter validation. Integrates with LangChain's model registry for declarative model selection in chains.","intents":["I want to switch between OpenAI models (gpt-4 → gpt-3.5-turbo) for cost optimization without refactoring","I need to use vision models for image understanding in the same chain as text models","I want to A/B test different models without duplicating chain logic"],"best_for":["Teams optimizing cost/performance tradeoffs by swapping models dynamically","Developers building multi-modal applications mixing text and vision models","Builders implementing model fallback strategies (try gpt-4, fall back to gpt-3.5-turbo)"],"limitations":["Model-specific features (vision, JSON mode) require explicit parameter handling; no automatic capability detection","Token counting differs across models; cost estimates may be inaccurate when switching models","No built-in model performance benchmarking — developers must manually compare output quality","Vision models have different input formats (base64, URLs) requiring preprocessing before API calls"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0","Model access via OpenAI API (gpt-4 requires separate access tier)"],"input_types":["Text messages","Image URLs or base64-encoded images (for vision models)","Mixed text and image content"],"output_types":["Text responses","Structured outputs (JSON mode)","Tool calls (function calling)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_7","uri":"capability://memory.knowledge.message.history.and.context.management.with.role.based.formatting","name":"message history and context management with role-based formatting","description":"Manages conversation history through LangChain's BaseMessage abstraction (HumanMessage, AIMessage, SystemMessage, ToolMessage) with automatic role-based formatting for OpenAI's API. Handles message serialization, deserialization, and context window management to prevent exceeding token limits. Integrates with LangChain's memory systems (ConversationBufferMemory, ConversationSummaryMemory) to persist and retrieve conversation context across turns.","intents":["I want to maintain conversation history across multiple LLM calls without manual message formatting","I need to manage context windows by trimming old messages when approaching token limits","I want to use different memory strategies (full history, summary, sliding window) without changing chain code"],"best_for":["Chatbot developers building multi-turn conversations","Teams implementing conversation persistence across sessions","Builders optimizing context window usage in long conversations"],"limitations":["No built-in message compression — long conversations require manual summarization or truncation","Memory systems add latency for retrieval and serialization; high-frequency updates can impact responsiveness","No automatic context window management — developers must manually trim messages or implement sliding windows","Tool message formatting requires explicit ToolMessage creation; no automatic tool result wrapping"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0","Memory backend (in-memory, database, or external store)"],"input_types":["BaseMessage objects (HumanMessage, AIMessage, SystemMessage, ToolMessage)","Plain strings (auto-wrapped as HumanMessage)","Message dicts with role/content"],"output_types":["Message lists formatted for OpenAI API","Serialized message history (JSON)","Trimmed/summarized message lists"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_8","uri":"capability://text.generation.language.prompt.template.compilation.and.variable.injection","name":"prompt template compilation and variable injection","description":"Integrates with LangChain's PromptTemplate system to enable declarative prompt definition with variable placeholders that are automatically injected at runtime. Supports Jinja2-style templating, conditional blocks, and dynamic prompt composition through LCEL chains. Compiles templates into Runnable objects that can be chained with ChatOpenAI models without manual string formatting.","intents":["I want to define reusable prompt templates with variables that inject at runtime","I need to compose complex prompts from multiple templates without string concatenation","I want to version control and test prompts separately from application code"],"best_for":["Teams managing multiple prompts across different use cases","Developers building prompt engineering workflows with version control","Builders implementing prompt testing and evaluation pipelines"],"limitations":["Template compilation adds ~10-20ms overhead per prompt; not suitable for ultra-high-throughput applications","No built-in prompt optimization or A/B testing — requires external tools for variant comparison","Variable validation is optional; missing or invalid variables may cause runtime errors","No automatic prompt caching — identical prompts with different variables require separate API calls"],"requires":["Python >=3.10","langchain-core >=1.2.7","openai >=1.0.0"],"input_types":["Prompt template strings with {variable} placeholders","Variable dicts for injection","Jinja2 template syntax"],"output_types":["Compiled PromptTemplate Runnable","Formatted prompt strings","Message lists ready for OpenAI API"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-openai__cap_9","uri":"capability://automation.workflow.langsmith.integration.for.tracing.and.debugging","name":"langsmith integration for tracing and debugging","description":"Integrates with LangSmith (LangChain's observability platform) to automatically trace LLM calls, tool invocations, and chain execution with structured logging. Captures inputs, outputs, latency, token usage, and errors without code changes through LangChain's callback system. Enables debugging complex chains by visualizing execution flow and identifying performance bottlenecks in LangSmith UI.","intents":["I want to trace LLM calls and see exactly what prompts and responses are being generated","I need to debug why my agent is taking unexpected actions or producing wrong outputs","I want to monitor LLM application performance and identify latency bottlenecks"],"best_for":["Teams debugging complex multi-step LLM chains","Developers monitoring production LLM applications","Builders implementing observability and debugging workflows"],"limitations":["LangSmith integration requires API key and network calls; adds ~50-100ms latency per trace","Tracing large chains with many steps can generate large amounts of data; may impact performance","No built-in data retention policies — long-running applications may accumulate excessive trace data","Requires LangSmith account and API access; not suitable for fully offline applications"],"requires":["Python >=3.10","langchain-core >=1.2.7","langsmith >=0.3.45","LANGCHAIN_API_KEY environment variable","LANGCHAIN_TRACING_V2=true to enable tracing"],"input_types":["Any LangChain Runnable (chains, models, tools)","Callback handlers"],"output_types":["Structured traces in LangSmith UI","Execution timelines","Token usage metrics","Error logs with context"],"categories":["automation-workflow","safety-moderation"],"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","openai >=1.0.0","pydantic >=2.7.4","OPENAI_API_KEY environment variable or explicit api_key parameter","OPENAI_API_KEY environment variable","openai >=1.0.0 with JSON mode support","Model supporting JSON mode (gpt-4-turbo, gpt-3.5-turbo-1106+)","openai >=1.0.0 with vision support","Access to gpt-4-vision or gpt-4-turbo models"],"failure_modes":["Adds ~50-100ms overhead per call due to Runnable abstraction layer and message serialization","No built-in caching of OpenAI responses — requires external integration with LangSmith or Redis","Structured output (JSON mode) requires manual schema definition; no automatic Pydantic-to-OpenAI schema conversion","Vision capabilities limited to what OpenAI API supports; no local image preprocessing","No local caching of embeddings — each unique text requires an API call unless wrapped with external cache","Batch size limited by OpenAI API (max 2048 texts per request); larger batches require manual chunking","Dimension reduction not supported — must use OpenAI's native dimensions (1536 for ada, 3072 for 3-large)","No built-in similarity search — requires separate vector store for retrieval","JSON mode requires explicit schema definition; complex nested models may exceed OpenAI's schema complexity limits","Fallback parsing adds latency and may fail on malformed JSON; native JSON mode is more reliable","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-openai","compare_url":"https://unfragile.ai/compare?artifact=pypi-langchain-openai"}},"signature":"/hDa3outLUHoyFTpNLFRxSEuXgvCq2OJ83u9dj/QGhCSruzaBKpTJq2koAcbH633BOWOIgQW2FzhYBNf2u9iAg==","signedAt":"2026-06-20T10:51:58.436Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langchain-openai","artifact":"https://unfragile.ai/pypi-langchain-openai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-langchain-openai","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"}}