{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-instructor","slug":"pypi-instructor","name":"instructor","type":"framework","url":"https://pypi.org/project/instructor/","page_url":"https://unfragile.ai/pypi-instructor","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-instructor__cap_0","uri":"capability://data.processing.analysis.schema.based.structured.output.validation.with.pydantic.models","name":"schema-based structured output validation with pydantic models","description":"Converts Pydantic model definitions into JSON schemas that constrain LLM outputs, then validates responses against those schemas before returning them to the user. Uses a decorator-based approach to wrap LLM calls, intercept raw outputs, parse them as JSON, and validate against the Pydantic model definition. Automatically handles schema generation, serialization, and type coercion.","intents":["I want my LLM to always return data in a specific structure (e.g., a list of extracted entities with fields)","I need to ensure LLM outputs are valid before passing them to downstream systems","I want to define output schemas once in Python and reuse them across multiple LLM calls"],"best_for":["Python developers building LLM applications with type-safe data pipelines","Teams extracting structured data from unstructured text using LLMs","Builders prototyping LLM agents that need deterministic output formats"],"limitations":["Pydantic v1 and v2 have different schema generation behaviors; migration requires code updates","Complex nested models with circular references may produce verbose schemas that exceed token limits","Validation happens post-generation, so invalid outputs waste tokens — no in-generation guidance","Schema size grows with model complexity, potentially exceeding context windows on smaller models"],"requires":["Python 3.8+","Pydantic 1.x or 2.x installed","API key for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)","LLM provider SDK (e.g., openai, anthropic)"],"input_types":["Python Pydantic BaseModel class definitions","Natural language prompts (strings)","LLM provider API parameters (temperature, max_tokens, etc.)"],"output_types":["Pydantic model instances (Python objects)","Validated JSON-serializable dictionaries","Type-checked Python dataclasses"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.client.patching.with.unified.interface","name":"multi-provider llm client patching with unified interface","description":"Wraps and patches official LLM provider SDKs (OpenAI, Anthropic, Cohere, etc.) to inject structured output validation into their native client methods without requiring code rewrites. Uses Python's monkey-patching and context managers to intercept API calls, inject schemas into prompts or system messages, and validate responses before returning them. Maintains compatibility with each provider's native API patterns.","intents":["I want to use structured outputs with my existing LLM provider client code with minimal changes","I need to switch between LLM providers without rewriting my validation logic","I want to add schema validation to an existing codebase that already uses OpenAI/Anthropic/etc SDKs"],"best_for":["Teams with existing LLM integrations looking to add structured outputs without refactoring","Developers building provider-agnostic LLM applications","Rapid prototypers who want to test multiple LLM providers with the same schema"],"limitations":["Patching approach is fragile to SDK version updates; breaking changes in provider APIs require instructor updates","Each provider has different schema injection mechanisms (some support JSON mode, others require prompt engineering)","Streaming responses require special handling and may not support full validation until the stream completes","Error handling varies by provider; some fail silently on schema violations while others throw exceptions"],"requires":["Python 3.8+","Official LLM provider SDK (openai>=1.0, anthropic>=0.7, cohere>=4.0, etc.)","Pydantic 1.x or 2.x","Valid API credentials for the target LLM provider"],"input_types":["LLM provider client instances (e.g., OpenAI(), Anthropic())","Pydantic model class definitions","Standard LLM API parameters (messages, temperature, max_tokens)"],"output_types":["Pydantic model instances","Validated response objects with structured data","Native provider response objects (wrapped)"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_10","uri":"capability://automation.workflow.async.await.support.for.concurrent.llm.operations","name":"async/await support for concurrent llm operations","description":"Provides async-compatible APIs for all LLM operations, enabling concurrent execution of multiple LLM calls without blocking. Uses Python's asyncio library to manage concurrent requests, with support for semaphores and rate limiting to avoid overwhelming the LLM provider. Maintains structured output validation across async calls.","intents":["I want to make multiple LLM calls concurrently to reduce total latency","I need to process streaming responses asynchronously without blocking my application","I want to rate-limit concurrent LLM calls to stay within API quotas"],"best_for":["High-performance applications requiring concurrent LLM operations","Async web frameworks (FastAPI, Starlette) integrating LLM calls","Applications processing multiple documents or queries in parallel"],"limitations":["Async code is more complex to debug and reason about than synchronous code","Rate limiting must be carefully tuned to avoid hitting provider quotas","Concurrent requests increase memory usage and may exceed provider connection limits","Error handling in async contexts is more complex; requires careful exception propagation"],"requires":["Python 3.8+ with asyncio support","Async-compatible LLM provider SDK (most modern SDKs support this)","Understanding of Python async/await patterns","Configured concurrency limits and rate limiting"],"input_types":["Async coroutines for LLM calls","Concurrency configuration (max concurrent requests, rate limit)","Pydantic model definitions"],"output_types":["Async generator or coroutine returning validated results","Gathered results from multiple concurrent calls","Exception if any concurrent call fails"],"categories":["automation-workflow","async-concurrency"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_2","uri":"capability://automation.workflow.automatic.retry.with.exponential.backoff.for.validation.failures","name":"automatic retry with exponential backoff for validation failures","description":"Automatically retries LLM calls when validation fails (e.g., output doesn't match schema), using exponential backoff with jitter to avoid rate limiting. Feeds validation error messages back into the prompt as context for the next attempt, allowing the LLM to self-correct. Configurable max retries, backoff multiplier, and timeout thresholds.","intents":["I want my LLM to automatically fix its output if it doesn't match my schema instead of failing","I need resilience against transient API errors and rate limiting","I want to give the LLM feedback about what went wrong so it can improve its next attempt"],"best_for":["Production LLM applications that need high reliability","Extraction pipelines where occasional LLM hallucinations are expected","Teams with strict SLAs who can't afford validation failures to crash the pipeline"],"limitations":["Retries increase total latency and token consumption; a 3-retry sequence can 3x the cost","Exponential backoff may not be appropriate for all error types (e.g., schema mismatch vs. API overload)","Max retries must be tuned per use case; too high wastes tokens, too low fails on valid edge cases","Feedback injection into prompts can cause prompt injection vulnerabilities if user input is not sanitized"],"requires":["Python 3.8+","Pydantic model definitions","LLM provider with API rate limiting (most providers)","Configured max_retries and backoff parameters"],"input_types":["LLM API call parameters","Pydantic model schema","Validation error messages (generated internally)"],"output_types":["Validated Pydantic model instance (after successful retry)","Exception with retry exhaustion details (if max retries exceeded)"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_3","uri":"capability://data.processing.analysis.streaming.response.validation.with.partial.schema.matching","name":"streaming response validation with partial schema matching","description":"Validates LLM outputs in real-time as they stream in, allowing partial schema validation and early error detection before the full response completes. Buffers streamed tokens, attempts to parse incomplete JSON, and validates against the schema incrementally. Supports yielding partial results as they become available while continuing to stream.","intents":["I want to start processing LLM outputs before the full response arrives to reduce latency","I need to detect validation errors early in the stream and stop generation if the output is going off-track","I want to yield partial results to the user while the LLM is still generating"],"best_for":["Real-time applications where latency is critical (chatbots, live dashboards)","Long-form generation tasks where early validation can save tokens","Streaming APIs that need to return results progressively"],"limitations":["Partial JSON parsing is fragile; incomplete objects may fail validation until the stream completes","Early error detection requires heuristics (e.g., detecting malformed JSON patterns) which may have false positives","Streaming validation adds CPU overhead for parsing and validation on every token chunk","Some LLM providers don't support interrupting streams mid-generation, limiting early error recovery"],"requires":["Python 3.8+","LLM provider with streaming API support (OpenAI, Anthropic, etc.)","Pydantic model definitions","Async/await support or threading for concurrent streaming"],"input_types":["Streaming LLM response iterator","Pydantic model schema","Chunk size and buffer configuration"],"output_types":["Async generator yielding partial Pydantic model instances","Validated complete model instance when stream ends","Exception if validation fails before stream completes"],"categories":["data-processing-analysis","streaming-validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_4","uri":"capability://tool.use.integration.function.calling.with.automatic.schema.generation.and.routing","name":"function calling with automatic schema generation and routing","description":"Converts Python functions and Pydantic models into tool schemas that LLMs can call, automatically generates the schema definitions, routes function calls based on LLM output, and executes them with type-safe argument binding. Supports both OpenAI-style tool calling and Anthropic-style function calling with unified interface. Handles argument validation, type coercion, and error propagation.","intents":["I want my LLM to call Python functions with the right arguments without manual schema definition","I need to expose a set of tools to the LLM and automatically route its function calls to the right handler","I want type-safe function calling where arguments are validated against the function signature"],"best_for":["LLM agents that need to interact with external APIs or Python functions","Teams building tool-using LLMs without manual schema engineering","Developers who want to expose Python functions to LLMs with minimal boilerplate"],"limitations":["Schema generation from function signatures may not capture all semantic constraints (e.g., valid value ranges, dependencies between arguments)","LLMs may hallucinate function names or arguments that don't exist; requires robust error handling","Recursive function calling (function calls that trigger more function calls) requires explicit loop handling","Type coercion from LLM-generated strings to Python types can fail silently or produce unexpected results"],"requires":["Python 3.8+","Pydantic 1.x or 2.x for schema generation","LLM provider with function calling support (OpenAI, Anthropic, Cohere)","Python functions or Pydantic models to expose as tools"],"input_types":["Python function definitions with type hints","Pydantic model class definitions","LLM provider function calling response"],"output_types":["Executed function results (any Python type)","Tool call routing decisions","Validated argument dictionaries"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_5","uri":"capability://data.processing.analysis.context.window.optimization.with.token.counting.and.truncation","name":"context window optimization with token counting and truncation","description":"Estimates token usage before sending requests to the LLM, truncates prompts or context to fit within the model's context window, and provides warnings when approaching limits. Uses provider-specific tokenizers (e.g., tiktoken for OpenAI) to count tokens accurately. Supports configurable truncation strategies (e.g., drop oldest messages, summarize, truncate tail).","intents":["I want to know how many tokens my prompt will use before sending it to avoid exceeding context limits","I need to automatically trim my context to fit within the model's context window","I want to optimize token usage to reduce costs while maintaining quality"],"best_for":["Cost-conscious teams running high-volume LLM applications","Developers building long-context applications (RAG, document analysis)","Teams with strict token budgets or SLAs"],"limitations":["Token counting is approximate; actual usage may differ by 1-5% due to tokenizer differences","Truncation strategies are lossy; dropping context may remove important information","Different models have different context windows; truncation logic must be model-aware","Token counting adds latency (~10-50ms per request) and requires downloading tokenizer models"],"requires":["Python 3.8+","Tokenizer library (tiktoken for OpenAI, or provider-specific tokenizers)","LLM provider with known context window size","Configured truncation strategy"],"input_types":["Prompt text (string or list of messages)","LLM model name","Context window size limit"],"output_types":["Token count estimate (integer)","Truncated prompt (string or message list)","Warning/error if context exceeds limit"],"categories":["data-processing-analysis","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_6","uri":"capability://automation.workflow.batch.processing.with.structured.output.validation","name":"batch processing with structured output validation","description":"Processes multiple LLM requests in parallel or sequentially with structured output validation, aggregating results and handling partial failures. Supports batching at the request level (multiple prompts) and response level (multiple outputs per prompt). Provides progress tracking, error aggregation, and retry logic per batch item.","intents":["I want to process a large dataset through an LLM with structured outputs efficiently","I need to handle partial failures in batch processing without losing all results","I want progress tracking and error reporting for batch LLM jobs"],"best_for":["Data processing pipelines extracting structured data from many documents","Batch classification or labeling tasks","Teams processing large datasets with LLMs in production"],"limitations":["Batch processing adds complexity for error handling; some items may succeed while others fail","Rate limiting and quota management must be handled explicitly to avoid API throttling","Memory usage grows with batch size; large batches may exceed available RAM","Debugging failures in large batches is difficult; requires detailed logging and error tracking"],"requires":["Python 3.8+","Pydantic model definitions","LLM provider with batch API or rate-limit-aware client","Configured batch size and concurrency limits"],"input_types":["List of prompts or input items","Pydantic model schema","Batch configuration (size, concurrency, retry policy)"],"output_types":["List of validated Pydantic model instances","Error report with failed items and reasons","Progress metrics (items processed, success rate)"],"categories":["automation-workflow","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_7","uri":"capability://data.processing.analysis.custom.validation.rules.and.post.processing.hooks","name":"custom validation rules and post-processing hooks","description":"Allows defining custom validation logic beyond schema conformance (e.g., business rules, semantic constraints) through validator decorators and post-processing hooks. Runs after Pydantic validation to enforce domain-specific rules, transform outputs, or enrich results. Supports chaining multiple validators and hooks with error aggregation.","intents":["I want to enforce business rules on LLM outputs (e.g., price must be > 0, email must be valid)","I need to transform or enrich LLM outputs before returning them to the user","I want to validate semantic constraints that can't be expressed in JSON schema"],"best_for":["Teams with complex domain-specific validation requirements","Applications that need to enforce business logic on LLM outputs","Developers building custom LLM pipelines with multi-stage validation"],"limitations":["Custom validators add complexity and maintenance burden; must be tested thoroughly","Validator errors don't automatically trigger retries; requires explicit integration with retry logic","Chaining multiple validators can create performance bottlenecks if validators are expensive","Debugging validator failures requires detailed error messages; poor error reporting makes troubleshooting hard"],"requires":["Python 3.8+","Pydantic 1.x or 2.x with validator support","Custom validator function definitions","Understanding of Pydantic's validator API"],"input_types":["Pydantic model instance (after schema validation)","Custom validator function","Post-processing hook function"],"output_types":["Validated and transformed Pydantic model instance","Validation error with custom error message","Enriched output with additional fields"],"categories":["data-processing-analysis","validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_8","uri":"capability://memory.knowledge.response.caching.with.semantic.deduplication","name":"response caching with semantic deduplication","description":"Caches LLM responses based on prompt similarity and model parameters, returning cached results for semantically similar prompts without re-querying the LLM. Uses embedding-based similarity matching or exact hash matching to identify duplicate requests. Supports configurable cache backends (in-memory, Redis, disk) and TTL policies.","intents":["I want to reduce LLM API costs by caching responses for similar prompts","I need to speed up repeated queries by returning cached results","I want to ensure consistency across multiple calls with the same intent"],"best_for":["High-volume applications with repeated or similar queries","Cost-sensitive deployments where reducing API calls is critical","Applications with predictable query patterns"],"limitations":["Semantic deduplication requires embedding models, adding latency and cost","Cache invalidation is complex; stale cached results may be returned if the LLM's knowledge changes","Cache backends add infrastructure complexity (Redis, databases) and operational overhead","Caching non-deterministic outputs (e.g., creative writing) may reduce quality by returning identical results"],"requires":["Python 3.8+","Cache backend (in-memory dict, Redis, or custom)","Optional: embedding model for semantic similarity","Configured cache TTL and similarity threshold"],"input_types":["LLM prompt (string or message list)","Model name and parameters","Cache configuration"],"output_types":["Cached response (if hit)","Fresh LLM response (if miss)","Cache metadata (hit/miss, age)"],"categories":["memory-knowledge","caching"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-instructor__cap_9","uri":"capability://automation.workflow.observability.and.logging.with.structured.tracing","name":"observability and logging with structured tracing","description":"Provides detailed logging and tracing of LLM calls, including prompts, responses, validation results, token usage, and latency. Integrates with observability platforms (e.g., Langfuse, OpenTelemetry) to export traces and metrics. Supports structured logging with JSON output for easy parsing and analysis.","intents":["I want to debug LLM behavior by seeing exactly what prompts were sent and what responses were received","I need to monitor token usage and costs across all LLM calls","I want to track latency and performance metrics for LLM operations"],"best_for":["Production LLM applications requiring debugging and monitoring","Teams tracking LLM costs and usage metrics","Developers optimizing LLM performance and latency"],"limitations":["Logging adds overhead (~5-20ms per request) and increases storage requirements","Structured tracing requires integration with external observability platforms","Sensitive data (prompts, responses) may be logged; requires careful data handling and compliance","Log volume can be high in high-throughput applications; requires log aggregation and filtering"],"requires":["Python 3.8+","Logging configuration (Python logging or custom handler)","Optional: observability platform (Langfuse, OpenTelemetry, etc.)","Configured log level and output format"],"input_types":["LLM call parameters","Response data","Validation results","Timing and token usage information"],"output_types":["Structured log entries (JSON or text)","Traces exported to observability platform","Metrics and analytics dashboards"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","Pydantic 1.x or 2.x installed","API key for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)","LLM provider SDK (e.g., openai, anthropic)","Official LLM provider SDK (openai>=1.0, anthropic>=0.7, cohere>=4.0, etc.)","Pydantic 1.x or 2.x","Valid API credentials for the target LLM provider","Python 3.8+ with asyncio support","Async-compatible LLM provider SDK (most modern SDKs support this)","Understanding of Python async/await patterns"],"failure_modes":["Pydantic v1 and v2 have different schema generation behaviors; migration requires code updates","Complex nested models with circular references may produce verbose schemas that exceed token limits","Validation happens post-generation, so invalid outputs waste tokens — no in-generation guidance","Schema size grows with model complexity, potentially exceeding context windows on smaller models","Patching approach is fragile to SDK version updates; breaking changes in provider APIs require instructor updates","Each provider has different schema injection mechanisms (some support JSON mode, others require prompt engineering)","Streaming responses require special handling and may not support full validation until the stream completes","Error handling varies by provider; some fail silently on schema violations while others throw exceptions","Async code is more complex to debug and reason about than synchronous code","Rate limiting must be carefully tuned to avoid hitting provider quotas","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-03T15:20:12.848Z","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-instructor","compare_url":"https://unfragile.ai/compare?artifact=pypi-instructor"}},"signature":"gWw1S0ttFaz24Goq97Pxfq5LIA8a9azFzDpmUKhPc9uATHicFLj7fq/tR55d9KbiVE3WKRp45H6VMHN8G5jgDQ==","signedAt":"2026-06-22T22:26:59.428Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-instructor","artifact":"https://unfragile.ai/pypi-instructor","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-instructor","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"}}