{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langchain-anthropic","slug":"pypi-langchain-anthropic","name":"langchain-anthropic","type":"framework","url":"https://pypi.org/project/langchain-anthropic/","page_url":"https://unfragile.ai/pypi-langchain-anthropic","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langchain-anthropic__cap_0","uri":"capability://tool.use.integration.claude.model.integration.via.langchain.runnable.interface","name":"claude model integration via langchain runnable interface","description":"Wraps Anthropic's Claude API endpoints (claude-3-opus, claude-3-sonnet, claude-3-haiku) as LangChain Runnable objects, enabling seamless composition within LangChain's expression language (LCEL). Implements the BaseLanguageModel abstraction with streaming support, token counting via Anthropic's API, and automatic retry logic through tenacity middleware. The integration translates LangChain's BaseMessage format (HumanMessage, AIMessage, SystemMessage) to Anthropic's native message protocol.","intents":["I want to use Claude models interchangeably with other LLMs in my LangChain chains without rewriting code","I need to compose Claude calls with retrievers, tools, and other LangChain components in a single pipeline","I want streaming responses from Claude integrated into my LangChain application","I need to track token usage across Claude API calls for cost monitoring"],"best_for":["LangChain developers building multi-model applications who want Claude as a drop-in alternative to OpenAI","Teams standardizing on LangChain abstractions and needing Anthropic provider support","Builders prototyping agentic systems that require model flexibility"],"limitations":["Requires langchain-core>=1.2.7 and langchain>=1.2.6, creating tight coupling to LangChain versioning","Streaming implementation adds ~50-100ms latency overhead compared to direct Anthropic SDK calls due to message translation layer","Token counting requires additional API call to Anthropic's count_tokens endpoint, adding latency for cost estimation","No built-in caching of model responses — requires external LangChain caching layer (Redis, in-memory) for deduplication"],"requires":["Python >=3.10.0, <4.0.0","langchain-core>=1.2.7","langchain>=1.2.6","Anthropic API key (ANTHROPIC_API_KEY environment variable)","pydantic>=2.7.4 for message validation"],"input_types":["BaseMessage objects (HumanMessage, AIMessage, SystemMessage, ToolMessage)","String prompts (auto-converted to HumanMessage)","Structured prompt templates with variable substitution"],"output_types":["AIMessage with content string","Streaming token-by-token via AsyncIterator[str]","Structured output via tool_choice and function calling"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_1","uri":"capability://tool.use.integration.tool.calling.with.anthropic.function.schema.binding","name":"tool calling with anthropic function schema binding","description":"Converts LangChain's BaseTool definitions into Anthropic's native tool_use format with automatic schema generation from Pydantic models. Handles bidirectional translation: LangChain tool definitions → Anthropic tool_use blocks → ToolMessage responses back into the conversation. Supports parallel tool execution and tool_choice constraints (required, auto, specific tool). The integration leverages Anthropic's native tool_use content blocks rather than function_calling wrappers, providing native support for multi-step tool interactions.","intents":["I want Claude to call external tools (APIs, databases, code execution) as part of my LangChain agent","I need to enforce that Claude must use a specific tool or set of tools in a given turn","I want to handle tool execution errors and feed them back to Claude for recovery","I need parallel tool execution where Claude calls multiple tools in a single response"],"best_for":["LangChain agent builders implementing ReAct or similar patterns with Claude as the reasoning engine","Teams building multi-step workflows where Claude orchestrates external tool calls","Developers needing native tool_use support without function_calling wrapper overhead"],"limitations":["Tool schema generation requires Pydantic v2 models; legacy function signatures not supported","Parallel tool execution requires manual orchestration of concurrent calls — no built-in async batching","Tool result feedback requires explicit ToolMessage construction; no automatic error wrapping","Maximum 10,000 tokens for tool definitions per request; complex tool sets may exceed limits"],"requires":["langchain-core>=1.2.7 with BaseTool and ToolMessage classes","Pydantic >=2.7.4 for tool schema validation","Anthropic API key with tool_use capability enabled","Claude model version supporting tool_use (claude-3-opus, claude-3-sonnet, claude-3-haiku)"],"input_types":["BaseTool objects with Pydantic-validated args_schema","Tool definitions as JSON schema (manual)","LangChain agent state with tool_calls in AIMessage.tool_calls"],"output_types":["Anthropic tool_use content blocks with tool_input JSON","ToolMessage responses with tool execution results","Error messages for failed tool invocations"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_10","uri":"capability://automation.workflow.async.await.support.with.concurrent.request.handling","name":"async/await support with concurrent request handling","description":"Provides full async/await support via agenerate, astream, and ainvoke methods, enabling concurrent Claude requests without blocking. Implements asyncio-compatible interfaces that integrate with LangChain's async chain execution. Supports concurrent tool execution, streaming, and batch operations within async contexts. Handles connection pooling and request queuing to optimize throughput for high-concurrency scenarios.","intents":["I want to make multiple Claude requests concurrently without blocking my application","I need to build async LangChain chains that scale to handle many concurrent users","I want to stream responses from multiple Claude requests simultaneously","I need to implement async tool execution where multiple tools run in parallel"],"best_for":["Web application developers building async APIs with FastAPI, Starlette, or similar frameworks","Teams implementing high-concurrency chat applications with many simultaneous users","Builders creating async agent systems with parallel tool execution"],"limitations":["Async implementation requires Python 3.10+; no support for older Python versions","Connection pooling adds ~50-100ms overhead for first request in async context due to pool initialization","Concurrent requests are subject to Anthropic's rate limits; no built-in backoff for rate limit errors","Async streaming requires event loop context; cannot be called from synchronous code without blocking"],"requires":["Python >=3.10.0","langchain-anthropic>=0.1.0 with async support","asyncio event loop","httpx>=0.24.0 for async HTTP support"],"input_types":["BaseMessage lists for async generation","Prompt strings for async completion","LangChain chains with async=True"],"output_types":["Coroutines returning AIMessage","AsyncIterator[str] for async streaming","Concurrent execution results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_11","uri":"capability://automation.workflow.callback.system.integration.for.observability.and.monitoring","name":"callback system integration for observability and monitoring","description":"Integrates with LangChain's callback system to emit events at each stage of Claude API calls: on_llm_start (before request), on_llm_new_token (during streaming), on_llm_end (after completion). Provides access to token usage, latency, error details, and model metadata through callback handlers. Supports custom callback implementations for logging, monitoring, tracing, and cost tracking. Integrates with LangSmith for production observability.","intents":["I want to log and monitor all Claude API calls in my LangChain application","I need to track latency, token usage, and costs across my chains","I want to implement custom error handling and retry logic based on API response details","I need to integrate with observability platforms (LangSmith, Datadog, etc.) for production monitoring"],"best_for":["Teams building production LLM applications requiring observability and monitoring","Developers implementing cost tracking and billing systems","Builders integrating with LangSmith or other observability platforms"],"limitations":["Callback overhead adds ~5-10ms per callback invocation; high-frequency callbacks may impact latency","Callbacks are synchronous by default; async callbacks require explicit async handler implementation","Callback errors do not propagate to the main chain; failed callbacks are silently ignored","No built-in filtering or sampling; all events are emitted regardless of verbosity level"],"requires":["langchain-anthropic>=0.1.0","langchain-core>=1.2.7 with callback system","Custom callback handler implementation (optional)"],"input_types":["BaseCallbackHandler subclasses","Event metadata (token counts, latency, model info)"],"output_types":["Callback events (on_llm_start, on_llm_new_token, on_llm_end)","Token usage and cost metrics","Error and exception details"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_2","uri":"capability://text.generation.language.streaming.response.generation.with.token.level.granularity","name":"streaming response generation with token-level granularity","description":"Implements streaming via Anthropic's server-sent events (SSE) protocol, yielding tokens as they arrive from the API with content_block_start, content_block_delta, and content_block_stop events. Translates Anthropic's streaming event types into LangChain's Runnable stream interface, supporting both sync (iter_final_text) and async (aiter_final_text) iteration. Handles mid-stream tool_use blocks and message deltas, preserving streaming semantics across complex multi-turn conversations.","intents":["I want to display Claude's response to users token-by-token for better perceived latency","I need to stream responses through my LangChain chain while maintaining composability with other components","I want to cancel long-running Claude requests mid-stream based on user input","I need to collect streaming tokens into a final AIMessage for downstream processing"],"best_for":["Web application developers building chat interfaces with real-time token streaming","Teams building LangChain chains where streaming must propagate through multiple components","Builders implementing cancellation logic for long-running Claude requests"],"limitations":["Streaming adds ~30-50ms latency before first token due to connection establishment and event parsing","Tool_use blocks within streams cannot be executed until the full tool_use block is received; no early execution","Streaming state is not persisted — if connection drops mid-stream, partial tokens are lost unless manually buffered","Async streaming requires event loop context; cannot be called from synchronous code without blocking"],"requires":["httpx>=0.24.0 for SSE streaming support","asyncio event loop for async streaming (aiter_final_text)","Anthropic API key with streaming capability","Network connection with keep-alive support for long-running streams"],"input_types":["BaseMessage list with streaming=True parameter","Prompt strings with stream=True flag","LangChain chain invocations with stream=True"],"output_types":["Iterator[str] for synchronous token streaming","AsyncIterator[str] for async token streaming","Final AIMessage after stream completion"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_3","uri":"capability://data.processing.analysis.message.format.translation.and.content.block.handling","name":"message format translation and content block handling","description":"Bidirectionally translates between LangChain's BaseMessage abstraction (HumanMessage, AIMessage, SystemMessage, ToolMessage) and Anthropic's native message protocol with content blocks (text, tool_use, tool_result). Handles special cases: system prompts as separate system parameter, tool_result blocks mapped from ToolMessage, multi-content AIMessages with interleaved text and tool_use blocks. Validates message sequences to ensure Anthropic protocol compliance (e.g., alternating human/assistant, tool_result only after tool_use).","intents":["I want to use LangChain's message abstraction without worrying about Anthropic API format differences","I need to preserve tool_use and tool_result semantics when translating between frameworks","I want to build multi-turn conversations with mixed content types (text, tool calls, results) seamlessly","I need validation that my message sequences comply with Anthropic's protocol requirements"],"best_for":["LangChain developers building multi-turn conversations with Claude who want abstraction over API details","Teams migrating from other LLM providers to Claude and needing transparent message format translation","Builders implementing custom message processing pipelines that need to understand both abstractions"],"limitations":["Translation adds ~10-20ms overhead per message due to validation and format conversion","LangChain's BaseMessage doesn't natively support Anthropic's content_block_source field; custom content is lost","System messages are extracted and passed separately; cannot be interleaved with user messages as in some Anthropic patterns","Tool_use blocks with complex nested structures may lose metadata during round-trip translation"],"requires":["langchain-core>=1.2.7 with BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage","pydantic>=2.7.4 for message validation","Anthropic API key"],"input_types":["LangChain BaseMessage objects (HumanMessage, AIMessage, SystemMessage, ToolMessage)","Message lists with mixed content types","Anthropic native message dicts (for reverse translation)"],"output_types":["Anthropic-formatted message dicts with content blocks","System parameter (string) extracted from SystemMessage","Validated message sequences compliant with Anthropic protocol"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_4","uri":"capability://text.generation.language.model.parameter.configuration.with.anthropic.specific.options","name":"model parameter configuration with anthropic-specific options","description":"Exposes Anthropic-specific model parameters (temperature, max_tokens, top_p, top_k, stop_sequences) through LangChain's model_kwargs interface, with validation and type coercion. Supports Anthropic-only features like thinking blocks (extended_thinking), budget_tokens for reasoning, and native tool_choice constraints. Parameters are passed through to Anthropic API calls without modification, enabling fine-grained control while maintaining LangChain abstraction compatibility.","intents":["I want to tune Claude's temperature, top_p, and other sampling parameters for my specific use case","I need to set max_tokens limits to control response length and API costs","I want to use Anthropic's extended_thinking feature for complex reasoning tasks","I need to enforce specific stop sequences to control response termination"],"best_for":["LangChain developers fine-tuning Claude behavior for specific tasks (summarization, reasoning, creative writing)","Teams building cost-optimized applications where max_tokens limits are critical","Builders leveraging Anthropic-specific features like extended_thinking that aren't available in other providers"],"limitations":["model_kwargs are Anthropic-specific; switching to OpenAI requires rewriting parameter names and values","Extended_thinking (thinking blocks) requires claude-3-7-sonnet or later; no fallback for older models","Budget_tokens for thinking is a soft limit; actual token usage may exceed budget without error","No validation that parameter combinations are valid for the selected model version"],"requires":["langchain-anthropic>=0.1.0","Anthropic API key","Claude model supporting desired parameters (e.g., claude-3-7-sonnet for extended_thinking)"],"input_types":["model_kwargs dict with Anthropic parameter names","Individual parameter values (temperature, max_tokens, etc.)","Extended_thinking config dict"],"output_types":["Validated parameter dict passed to Anthropic API","API response with usage metadata reflecting parameter effects"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_5","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.via.anthropic.api","name":"token counting and cost estimation via anthropic api","description":"Calls Anthropic's count_tokens API endpoint to accurately count input and output tokens before and after API calls, enabling precise cost calculation. Integrates with LangChain's callback system to track token usage across chains. Supports batch token counting for multiple messages, with caching of count results to avoid redundant API calls. Returns token counts broken down by input, output, and cache usage (for prompt caching).","intents":["I want to estimate API costs before making Claude requests to my users","I need to track token usage across my LangChain chain for billing and monitoring","I want to optimize prompts by measuring token counts for different phrasings","I need to understand cache hit rates and cache token usage for cost optimization"],"best_for":["Teams building production LLM applications where API costs must be tracked and optimized","Developers implementing cost-aware routing (e.g., use cheaper model if token count exceeds threshold)","Builders using Anthropic's prompt caching and needing to measure cache effectiveness"],"limitations":["count_tokens API call adds ~100-200ms latency per token counting request; not suitable for real-time token counting","Token counts are approximate for streaming responses; final count may differ from pre-call estimate","Cache token counts require separate API call to measure; no built-in cache hit prediction","Batch token counting has no API endpoint; must call count_tokens separately for each message sequence"],"requires":["Anthropic API key with count_tokens capability","langchain-anthropic>=0.1.0","Network connectivity to Anthropic API"],"input_types":["BaseMessage lists for token counting","System prompts and user messages","Tool definitions for tool_use token overhead"],"output_types":["Token count dict with input_tokens, output_tokens, cache_creation_input_tokens, cache_read_input_tokens","Cost estimate in USD (if pricing data available)","Usage metadata for callback system"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_6","uri":"capability://data.processing.analysis.structured.output.with.json.schema.validation","name":"structured output with json schema validation","description":"Enables Claude to return structured JSON output by specifying a JSON schema constraint via Anthropic's native schema parameter. Automatically validates Claude's response against the schema and parses JSON into Python objects. Integrates with LangChain's output parsing system, allowing structured outputs to be chained with downstream components that expect typed data. Supports nested schemas, arrays, and complex type hierarchies via Pydantic model definitions.","intents":["I want Claude to return structured data (JSON) that I can immediately use in my application without parsing","I need to enforce that Claude's response conforms to a specific schema for downstream processing","I want to extract entities, relationships, or other structured information from unstructured text","I need to validate Claude's output before passing it to other components in my chain"],"best_for":["LangChain developers building data extraction pipelines with Claude","Teams implementing structured output requirements for API responses","Builders creating multi-step workflows where downstream components require typed inputs"],"limitations":["Schema validation adds ~50-100ms overhead due to JSON parsing and Pydantic validation","Complex nested schemas may exceed Anthropic's schema size limits (no documented limit, but practical ~50KB)","Claude may refuse to follow schema constraints if they conflict with safety guidelines","Schema validation errors are not automatically recovered; require explicit error handling in chains"],"requires":["langchain-anthropic>=0.1.0","Pydantic >=2.7.4 for schema definition","Claude model supporting schema constraints (claude-3-opus, claude-3-sonnet, claude-3-haiku)","Anthropic API key"],"input_types":["Pydantic BaseModel classes defining output schema","JSON schema dicts (manual)","LangChain prompt templates with schema context"],"output_types":["Parsed Python objects matching schema (Pydantic models)","JSON strings conforming to schema","Validation errors if response doesn't match schema"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_7","uri":"capability://automation.workflow.batch.processing.with.anthropic.batch.api.integration","name":"batch processing with anthropic batch api integration","description":"Wraps Anthropic's Batch API for asynchronous processing of multiple requests with 50% cost savings. Converts LangChain chains into batch request format, submits to Anthropic's batch queue, and polls for completion. Handles batch status tracking, result retrieval, and error aggregation across requests. Integrates with LangChain's async interfaces to enable non-blocking batch submission and result collection.","intents":["I want to process large volumes of Claude requests at lower cost using the Batch API","I need to submit multiple independent chains for asynchronous processing without blocking","I want to track batch job status and retrieve results as they complete","I need to handle failures and retries within batch processing workflows"],"best_for":["Teams processing large document sets, bulk data extraction, or batch inference at scale","Builders implementing cost-optimized data pipelines where latency is not critical","Applications with predictable batch workloads that can tolerate 24-hour processing windows"],"limitations":["Batch API has 24-hour processing window; not suitable for real-time or near-real-time applications","Minimum batch size is 1 request, but cost savings only meaningful at 100+ requests per batch","No streaming support within batch requests; all responses are buffered until batch completion","Batch request format requires manual conversion from LangChain chains; no automatic batching of existing chains"],"requires":["langchain-anthropic>=0.1.0 with batch support","Anthropic API key with Batch API access","asyncio event loop for async batch operations","Persistent storage for batch job IDs (for result retrieval across sessions)"],"input_types":["List of BaseMessage sequences for batch processing","LangChain chain invocations with batch=True","Batch request dicts in Anthropic format"],"output_types":["Batch job ID for tracking","Batch status (processing, completed, failed)","List of AIMessage results matching input order","Error details for failed requests"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_8","uri":"capability://image.visual.vision.capability.with.image.input.handling","name":"vision capability with image input handling","description":"Enables Claude to process images by converting LangChain's image content blocks into Anthropic's native image format with base64 encoding or URL references. Supports multiple image types (JPEG, PNG, GIF, WebP) and automatically detects media type. Integrates with LangChain's HumanMessage content arrays, allowing mixed text and image inputs in single messages. Handles image size constraints and automatically downsamples oversized images to meet API limits.","intents":["I want Claude to analyze images as part of my LangChain chain","I need to build document understanding pipelines that process PDFs and images with Claude","I want to ask Claude questions about images in a multi-turn conversation","I need to extract text, objects, or structured data from images using Claude's vision"],"best_for":["LangChain developers building document analysis and OCR pipelines with Claude","Teams implementing visual question-answering systems","Builders creating multi-modal applications that combine text and image understanding"],"limitations":["Image processing adds ~200-500ms latency per image due to encoding and API transmission","Maximum image size is 5MB; larger images must be downsampled or split","GIF and WebP support requires Claude 3.5 Sonnet or later; older models support JPEG/PNG only","Image content is not cached by default; each request re-encodes and transmits the full image"],"requires":["langchain-anthropic>=0.1.0 with vision support","Claude model with vision capability (claude-3-opus, claude-3-sonnet, claude-3-haiku)","Anthropic API key","PIL/Pillow for image processing and format detection"],"input_types":["Image URLs (http/https)","Base64-encoded image data","Local file paths (converted to base64)","PIL Image objects"],"output_types":["Text descriptions of image content","Structured data extracted from images (via JSON schema)","Answers to questions about image content"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langchain-anthropic__cap_9","uri":"capability://memory.knowledge.prompt.caching.for.repeated.context.optimization","name":"prompt caching for repeated context optimization","description":"Implements Anthropic's prompt caching feature by automatically identifying repeated system prompts and long context blocks, marking them for caching via the cache_control parameter. Tracks cache hits and misses across requests, enabling cost savings for applications with stable system prompts or repeated document context. Integrates with LangChain's callback system to expose cache metrics (cache_creation_input_tokens, cache_read_input_tokens) for monitoring cache effectiveness.","intents":["I want to reduce API costs by caching repeated system prompts and context across multiple requests","I need to measure cache hit rates and cache effectiveness in my LangChain application","I want to optimize document-heavy workflows where the same context is queried multiple times","I need to understand cache token usage for cost optimization"],"best_for":["Teams building RAG systems where the same documents are queried repeatedly","Applications with stable system prompts that are reused across many requests","Builders implementing cost-optimized inference where cache hits provide 90% token cost reduction"],"limitations":["Prompt caching requires minimum 1024 input tokens before caching is effective; small prompts don't benefit","Cache entries expire after 5 minutes of inactivity; long-running applications must refresh cache periodically","Cache invalidation is not automatic; changing system prompt or context requires manual cache reset","Cache overhead (1% of cached tokens) applies even on cache hits; only worthwhile for repeated queries"],"requires":["langchain-anthropic>=0.1.0 with caching support","Claude model supporting prompt caching (claude-3-5-sonnet or later)","Anthropic API key","Minimum 1024 input tokens per request for caching to be effective"],"input_types":["System prompts marked for caching","Long context blocks (documents, retrieved passages)","Repeated message sequences"],"output_types":["Cache metrics (cache_creation_input_tokens, cache_read_input_tokens)","Cost savings estimate based on cache hits","Cache status (hit, miss, creation)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python >=3.10.0, <4.0.0","langchain-core>=1.2.7","langchain>=1.2.6","Anthropic API key (ANTHROPIC_API_KEY environment variable)","pydantic>=2.7.4 for message validation","langchain-core>=1.2.7 with BaseTool and ToolMessage classes","Pydantic >=2.7.4 for tool schema validation","Anthropic API key with tool_use capability enabled","Claude model version supporting tool_use (claude-3-opus, claude-3-sonnet, claude-3-haiku)","Python >=3.10.0"],"failure_modes":["Requires langchain-core>=1.2.7 and langchain>=1.2.6, creating tight coupling to LangChain versioning","Streaming implementation adds ~50-100ms latency overhead compared to direct Anthropic SDK calls due to message translation layer","Token counting requires additional API call to Anthropic's count_tokens endpoint, adding latency for cost estimation","No built-in caching of model responses — requires external LangChain caching layer (Redis, in-memory) for deduplication","Tool schema generation requires Pydantic v2 models; legacy function signatures not supported","Parallel tool execution requires manual orchestration of concurrent calls — no built-in async batching","Tool result feedback requires explicit ToolMessage construction; no automatic error wrapping","Maximum 10,000 tokens for tool definitions per request; complex tool sets may exceed limits","Async implementation requires Python 3.10+; no support for older Python versions","Connection pooling adds ~50-100ms overhead for first request in async context due to pool initialization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.9,"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-anthropic","compare_url":"https://unfragile.ai/compare?artifact=pypi-langchain-anthropic"}},"signature":"ov5ADK8nrJmGiUIiL9GBy2B7q5kg8Ob+UJuYp7VrdTKoLHruNAVFBPa9wqdufcokUFBXGpxo6MVZNuLpc6bFCw==","signedAt":"2026-06-15T21:24:13.319Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langchain-anthropic","artifact":"https://unfragile.ai/pypi-langchain-anthropic","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-langchain-anthropic","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"}}