{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-rehydra","slug":"rehydra","name":"rehydra","type":"repo","url":"https://github.com/rehydra-ai/rehydra-sdk","page_url":"https://unfragile.ai/rehydra","categories":["data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-rehydra__cap_0","uri":"capability://safety.moderation.local.pii.anonymization.before.llm.transmission","name":"local-pii-anonymization-before-llm-transmission","description":"Intercepts prompts before they reach LLM APIs and applies pattern-based PII detection and replacement with deterministic tokens (e.g., [PERSON_1], [EMAIL_2]) using configurable regex and NER-style matching rules. The anonymization happens entirely on the client side with zero data transmission to external services, maintaining a local mapping table for later rehydration. Supports multiple PII categories (names, emails, phone numbers, SSNs, credit cards, API keys) with pluggable detection strategies.","intents":["I need to send sensitive customer data to Claude/GPT without exposing PII to the LLM provider","I want to comply with data residency requirements while still using cloud LLMs","I need to audit exactly what PII was sent to external APIs and maintain a local record","I want to prevent accidental leakage of API keys, credentials, or personal information in prompts"],"best_for":["enterprises handling regulated data (healthcare, finance, legal) that must use LLMs","teams building AI applications with strict data governance requirements","developers integrating LLMs into systems where PII exposure is a compliance violation","organizations needing audit trails of what data touched external services"],"limitations":["Pattern-based detection has false positive/negative rates — context-dependent PII (e.g., 'John' as a product name) may be incorrectly flagged","Rehydration assumes deterministic token mapping — if the same PII appears multiple times, it will be replaced with the same token, potentially leaking patterns","No built-in handling of PII in structured data formats (JSON, XML) — requires pre-processing or custom serializers","Performance degrades with very large prompts (>100KB) due to regex scanning overhead","Does not anonymize LLM responses by default — requires explicit configuration to detect PII in model outputs"],"requires":["Python 3.8+ or Node.js 16+","No external API keys required for anonymization itself (zero-trust design)","LLM provider API key (OpenAI, Anthropic, etc.) for actual LLM calls"],"input_types":["text (raw prompts)","structured prompts (with metadata about PII locations)","code snippets (for detecting hardcoded secrets)"],"output_types":["anonymized text (with tokens replacing PII)","mapping table (token → original PII, stored locally)","audit log (what was anonymized, when, by whom)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_1","uri":"capability://data.processing.analysis.deterministic.pii.rehydration.in.llm.responses","name":"deterministic-pii-rehydration-in-llm-responses","description":"Automatically reverses the anonymization process by mapping anonymized tokens (e.g., [PERSON_1]) back to their original PII values using the locally-stored mapping table generated during the anonymization phase. Uses exact token matching and position-aware replacement to restore context while preserving LLM-generated content. Supports partial rehydration (selectively restore only certain PII categories) and validation to ensure no tokens remain unrehydrated.","intents":["I need to convert the LLM's anonymized response back to use real names, emails, and other PII","I want to ensure the LLM's output is fully rehydrated before returning it to the user","I need to selectively rehydrate only certain PII types (e.g., names but not SSNs) based on downstream permissions","I want to detect if the LLM accidentally generated new PII that wasn't in the original prompt"],"best_for":["applications where the final user-facing output must contain real PII (e.g., customer service responses)","workflows that anonymize for LLM processing but need to restore data for downstream systems","teams implementing fine-grained access control over which PII categories are rehydrated"],"limitations":["Rehydration is only as accurate as the anonymization mapping — if anonymization missed PII, rehydration cannot recover it","If the LLM generates new text containing anonymized tokens (e.g., 'The person [PERSON_1] called [PERSON_2]'), rehydration may incorrectly replace them","No built-in handling of PII that appears in different contexts (e.g., same name used as both a person and a product) — requires manual disambiguation","Rehydration performance is O(n) in the number of tokens and mapping entries — large mappings (>10K entries) may add noticeable latency"],"requires":["Anonymization mapping table from the anonymization phase (must be persisted or passed through the same session)","Python 3.8+ or Node.js 16+","Access to the original anonymized LLM response"],"input_types":["anonymized text (LLM response with [TOKEN_N] placeholders)","mapping table (token → original PII)","optional: rehydration policy (which PII categories to restore)"],"output_types":["rehydrated text (with original PII restored)","audit log (which tokens were rehydrated, which were skipped)","validation report (any unrehydrated tokens or new PII detected)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_10","uri":"capability://data.processing.analysis.pii.detection.in.structured.data.and.code","name":"pii-detection-in-structured-data-and-code","description":"Extends PII detection beyond plain text to structured formats (JSON, XML, CSV) and code (Python, JavaScript, SQL), with format-aware parsing that understands data structure and can anonymize specific fields or values. Detects hardcoded secrets (API keys, database passwords) in code and configuration files. Supports custom field mappings (e.g., 'email' field always contains email PII) to improve detection accuracy in structured data.","intents":["I need to anonymize JSON/XML responses from APIs before sending them to the LLM","I want to detect and remove hardcoded API keys and secrets from code snippets before processing","I need to anonymize specific fields in CSV files (e.g., the 'email' column) without touching other columns","I want to handle SQL queries that contain PII in WHERE clauses or VALUES"],"best_for":["applications that process structured data (JSON APIs, databases, CSV exports)","code analysis tools that need to detect and remove secrets","data pipelines that work with multiple data formats"],"limitations":["Format-aware parsing adds complexity — each format (JSON, XML, CSV) requires custom parsing logic","Field mapping is manual — users must specify which fields contain PII, or detection falls back to pattern matching","Code parsing is language-specific — detecting secrets in Python requires different logic than JavaScript or SQL","Nested structures (e.g., JSON with deeply nested objects) require recursive traversal, which can be slow for large documents"],"requires":["Python 3.8+ or Node.js 16+","Optional: json, xml, csv libraries (usually built-in)","Optional: AST parsing libraries (ast for Python, @babel/parser for JavaScript) for code analysis"],"input_types":["structured data (JSON, XML, CSV)","code snippets (Python, JavaScript, SQL, etc.)","optional: field mapping (field name → PII type)"],"output_types":["anonymized structured data (preserving format)","detected secrets (with location and type)","mapping table (field value → token)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_11","uri":"capability://data.processing.analysis.pii.redaction.with.visual.feedback","name":"pii-redaction-with-visual-feedback","description":"Provides visual indicators (highlighting, strikethrough, color coding) in text and structured data to show which parts were anonymized, useful for debugging and validation. Supports multiple visual styles (inline redaction, margin notes, separate redaction report) and can generate side-by-side comparisons of original and anonymized text. Enables interactive redaction review where users can approve or reject individual anonymizations before sending to the LLM.","intents":["I want to visually review which parts of my prompt were anonymized before sending to the LLM","I need to debug why a particular string was or wasn't anonymized","I want to show stakeholders exactly what data was protected before sending to an external service","I need to manually approve or reject individual anonymizations before they're applied"],"best_for":["interactive applications where users need to review anonymization before proceeding","debugging and validation workflows where visibility into anonymization decisions is critical","compliance demonstrations where stakeholders need to see what data was protected"],"limitations":["Visual feedback is primarily useful for human review — not applicable in fully automated workflows","Generating visual comparisons adds latency (~50-200ms depending on document size)","Interactive approval requires user input, blocking the anonymization pipeline — not suitable for real-time applications","Visual styles are text-based — not applicable to binary or non-text data"],"requires":["Python 3.8+ or Node.js 16+","Optional: HTML/CSS rendering for web-based visual feedback"],"input_types":["original text","anonymization decisions (which parts to redact)","optional: visual style preferences"],"output_types":["visually annotated text (with redaction indicators)","side-by-side comparison (original vs. anonymized)","redaction report (summary of what was redacted)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.pii.handling","name":"multi-provider-llm-integration-with-pii-handling","description":"Provides a unified abstraction layer that wraps LLM provider APIs (OpenAI, Anthropic, Cohere, etc.) with automatic PII anonymization before sending requests and rehydration after receiving responses. Implements provider-agnostic request/response transformation using adapter patterns, allowing the same anonymization logic to work across different LLM APIs without code changes. Handles provider-specific response formats (streaming vs. batch, token counts, function calling) transparently.","intents":["I want to use multiple LLM providers (GPT, Claude, Cohere) without duplicating anonymization logic","I need to switch LLM providers without rewriting my PII handling code","I want to anonymize prompts for one provider and rehydrate responses from another provider in a multi-step workflow","I need to handle provider-specific features (streaming, function calling) while maintaining PII protection"],"best_for":["teams evaluating or using multiple LLM providers and wanting consistent PII handling across all","applications that need to switch providers dynamically based on cost, latency, or availability","enterprises with multi-vendor LLM strategies that need unified compliance and audit trails"],"limitations":["Adapter layer adds ~50-100ms latency per request due to request/response transformation","Not all provider-specific features are supported — advanced features like vision, function calling, or tool use may require custom adapters","Streaming responses require buffering to perform rehydration, eliminating the latency benefit of streaming","Provider API rate limits and quota management are not abstracted — must be handled separately per provider"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)","Python 3.8+ or Node.js 16+","Network access to LLM provider APIs"],"input_types":["prompts (text or structured)","provider configuration (API key, model name, parameters)","optional: custom adapter for unsupported providers"],"output_types":["LLM responses (text, structured, or streaming)","metadata (tokens used, latency, provider)","audit logs (which provider was used, what was anonymized)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_3","uri":"capability://data.processing.analysis.configurable.pii.detection.rules.and.patterns","name":"configurable-pii-detection-rules-and-patterns","description":"Allows users to define custom PII detection rules using regex patterns, NER models, or custom Python/JavaScript functions, with support for category-based organization (names, emails, phone numbers, custom types). Rules are composable and can be enabled/disabled per request, supporting both built-in patterns (SSN, credit card, email) and domain-specific patterns (medical record numbers, internal employee IDs). Configuration can be loaded from files (YAML, JSON) or defined programmatically.","intents":["I need to detect domain-specific PII that isn't covered by built-in patterns (e.g., internal employee IDs, medical record numbers)","I want to customize detection sensitivity (e.g., detect all email-like patterns vs. only known domains)","I need to enable/disable certain PII categories for different use cases (e.g., anonymize names but not emails)","I want to use a custom NER model or third-party PII detection service alongside built-in patterns"],"best_for":["organizations with domain-specific PII requirements (healthcare, finance, legal)","teams that need fine-grained control over what gets anonymized","developers integrating rehydra into existing systems with custom PII definitions"],"limitations":["Custom regex patterns require careful testing — overly broad patterns cause false positives, overly narrow patterns miss real PII","NER-based detection requires loading and running ML models, adding ~500ms-2s latency per request depending on model size","No built-in validation of regex patterns — invalid patterns will fail at runtime","Custom function-based detection has no performance guarantees — slow functions will block the entire anonymization pipeline"],"requires":["Python 3.8+ or Node.js 16+","Optional: spaCy, transformers, or other NER libraries if using ML-based detection","Optional: YAML/JSON configuration files for rule definitions"],"input_types":["regex patterns (as strings or compiled patterns)","NER model references (spaCy model names, HuggingFace model IDs)","custom detection functions (Python callables or JavaScript functions)","configuration files (YAML, JSON)"],"output_types":["detection rules (compiled and ready to use)","PII matches (with category, position, confidence score)","validation report (which rules matched, which failed)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_4","uri":"capability://memory.knowledge.session.based.pii.mapping.persistence","name":"session-based-pii-mapping-persistence","description":"Maintains a session-scoped mapping table that tracks all PII-to-token conversions within a single conversation or workflow, enabling consistent anonymization across multiple prompts and responses. Supports multiple persistence backends (in-memory, file-based, Redis, database) with automatic cleanup and optional encryption of stored mappings. Provides APIs to export, import, and audit the mapping history for compliance and debugging.","intents":["I need to ensure the same person's name is anonymized to the same token across multiple turns in a conversation","I want to persist the PII mapping so I can rehydrate responses hours or days later","I need to audit which PII was sent to the LLM in a multi-turn conversation","I want to share anonymized conversations with other team members while keeping the mapping secure"],"best_for":["multi-turn conversational AI applications where consistency is critical","workflows that span multiple sessions or require long-term PII mapping storage","compliance-heavy applications that need detailed audit trails of PII handling"],"limitations":["In-memory persistence is lost when the process terminates — requires explicit export for durability","Encrypted persistence adds encryption/decryption overhead (~10-50ms per operation depending on key size)","No built-in garbage collection — old mappings must be manually cleaned up or will accumulate indefinitely","Distributed systems require a shared persistence backend (Redis, database) — in-memory persistence does not work across multiple processes"],"requires":["Python 3.8+ or Node.js 16+","Optional: Redis, PostgreSQL, or other database for shared persistence","Optional: encryption library (cryptography, TweetNaCl) if using encrypted persistence"],"input_types":["session ID (string identifier for the conversation/workflow)","PII-to-token mappings (generated during anonymization)","optional: encryption key (for encrypted persistence)"],"output_types":["mapping table (token → original PII)","audit log (when each mapping was created, by whom, for what purpose)","export format (JSON, CSV, or encrypted binary)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_5","uri":"capability://data.processing.analysis.streaming.response.anonymization.and.rehydration","name":"streaming-response-anonymization-and-rehydration","description":"Handles streaming LLM responses (e.g., OpenAI's streaming API) by buffering tokens incrementally and applying rehydration on-the-fly as chunks arrive, without waiting for the complete response. Uses a token-aware buffer that detects partial tokens and ensures rehydration happens at token boundaries, maintaining stream semantics while protecting PII. Supports both server-sent events (SSE) and WebSocket streaming protocols.","intents":["I want to stream LLM responses to the user while still rehydrating PII in real-time","I need to detect if the LLM generates new PII in streaming responses and handle it gracefully","I want to maintain low latency for streaming while ensuring all PII is properly rehydrated","I need to support streaming in web applications (SSE) and real-time APIs (WebSocket)"],"best_for":["real-time chat applications where streaming latency is critical","web applications using Server-Sent Events (SSE) for streaming responses","applications that need to show LLM responses incrementally while maintaining PII protection"],"limitations":["Streaming rehydration requires buffering at least one token at a time, adding ~10-50ms latency compared to non-streaming","If a PII token spans multiple chunks (e.g., '[PERSON' in one chunk, '_1]' in the next), rehydration may fail or require complex lookahead logic","Streaming prevents batch optimizations — each chunk is processed independently, reducing opportunities for optimization","Error handling is complex — if rehydration fails mid-stream, the stream is corrupted and cannot be recovered"],"requires":["Python 3.8+ or Node.js 16+","LLM provider with streaming API support (OpenAI, Anthropic, Cohere)","Optional: asyncio (Python) or async/await (JavaScript) for non-blocking streaming"],"input_types":["streaming response chunks (text or binary)","mapping table (for rehydration)","optional: chunk size hints (for optimization)"],"output_types":["rehydrated streaming chunks (text or binary)","metadata (tokens processed, latency per chunk)","error events (if rehydration fails)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_6","uri":"capability://data.processing.analysis.pii.detection.confidence.scoring.and.filtering","name":"pii-detection-confidence-scoring-and-filtering","description":"Assigns confidence scores (0-1) to detected PII based on pattern specificity, context, and detection method (regex vs. NER), allowing users to filter detections by confidence threshold. Supports multiple scoring strategies (pattern-based, model-based, ensemble) and provides detailed reasoning for each detection (why it was flagged, which rule matched). Enables tuning of false positive/negative rates by adjusting thresholds per PII category.","intents":["I want to reduce false positives by only anonymizing high-confidence PII detections","I need to understand why a particular string was flagged as PII (for debugging and validation)","I want to use different confidence thresholds for different PII categories (strict for SSNs, lenient for names)","I need to measure and report the accuracy of my PII detection rules"],"best_for":["applications where false positives are costly (e.g., anonymizing product names that happen to match email patterns)","teams that need to audit and validate PII detection rules","workflows that require different sensitivity levels for different PII types"],"limitations":["Confidence scoring is heuristic-based and not calibrated to actual false positive rates — thresholds must be tuned empirically","NER-based confidence scores depend on the model's calibration — different models may produce different scores for the same input","No built-in way to measure true positive/false positive rates without manually labeled data","Confidence filtering may miss real PII if thresholds are set too high, creating a false sense of security"],"requires":["Python 3.8+ or Node.js 16+","Optional: spaCy or transformers for NER-based confidence scoring"],"input_types":["detected PII matches (with pattern/model information)","confidence threshold (0-1, per category or global)","optional: labeled examples for threshold calibration"],"output_types":["filtered PII matches (only those above threshold)","confidence scores (per match)","detection reasoning (which rule/model matched, why)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_7","uri":"capability://safety.moderation.audit.logging.and.compliance.reporting","name":"audit-logging-and-compliance-reporting","description":"Automatically logs all PII anonymization and rehydration operations with timestamps, user IDs, operation type, and affected data categories, enabling compliance audits and forensic analysis. Supports multiple log destinations (file, syslog, cloud logging services) and formats (JSON, CSV, structured logs). Provides pre-built compliance reports (GDPR, HIPAA, SOC 2) that summarize PII handling activities and demonstrate data protection measures.","intents":["I need to prove to auditors that PII was never sent to external LLM services","I want to generate compliance reports showing how PII was handled in my application","I need to investigate a security incident by reviewing the audit log of PII operations","I want to track which users accessed or anonymized which PII for accountability"],"best_for":["regulated industries (healthcare, finance, legal) with compliance requirements","enterprises with security and audit teams that need detailed logs","applications handling sensitive data where accountability is critical"],"limitations":["Audit logs themselves contain sensitive information (which PII was anonymized) — must be encrypted and access-controlled","Logging adds overhead (~5-20ms per operation depending on log destination) — high-volume applications may see latency impact","Pre-built compliance reports are templates — actual compliance requires legal review and may require customization","Log retention policies must be managed separately — rehydra does not enforce retention or deletion"],"requires":["Python 3.8+ or Node.js 16+","Optional: cloud logging service (AWS CloudWatch, Google Cloud Logging, etc.) for centralized logging","Optional: encryption library for securing audit logs"],"input_types":["anonymization/rehydration operations (with metadata)","user context (user ID, session ID, request ID)","optional: custom log fields"],"output_types":["audit logs (JSON, CSV, or structured format)","compliance reports (GDPR, HIPAA, SOC 2 templates)","analytics (summary statistics on PII handling)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_8","uri":"capability://data.processing.analysis.batch.pii.anonymization.and.rehydration","name":"batch-pii-anonymization-and-rehydration","description":"Processes multiple prompts and responses in batch mode, applying anonymization and rehydration to all items in a single operation with shared PII mappings. Optimizes performance by building a unified PII detection index across all inputs, reducing redundant pattern matching. Supports parallel processing for large batches and provides progress tracking and error handling per item.","intents":["I need to anonymize a large dataset of customer conversations before sending them to an LLM for analysis","I want to process 1000+ prompts efficiently without running anonymization separately for each one","I need to ensure consistent PII mapping across a batch of related conversations","I want to parallelize anonymization to reduce total processing time"],"best_for":["batch processing workflows (e.g., nightly jobs that process accumulated conversations)","data analysis pipelines that need to anonymize large datasets before LLM processing","teams processing historical data for compliance or research purposes"],"limitations":["Batch processing requires loading all items into memory — very large batches (>100K items) may cause memory issues","Shared PII mappings across a batch mean that the same person's name is always anonymized to the same token, which may leak patterns if the batch is later analyzed","Parallel processing adds complexity and may not be faster than sequential processing for small batches due to overhead","Error handling is all-or-nothing — if one item fails, the entire batch may be rolled back"],"requires":["Python 3.8+ or Node.js 16+","Optional: multiprocessing (Python) or worker threads (JavaScript) for parallel processing"],"input_types":["list of prompts/responses (text or structured)","optional: batch configuration (parallel workers, chunk size)"],"output_types":["anonymized items (with consistent PII mapping)","shared mapping table (for the entire batch)","progress report (items processed, errors, timing)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rehydra__cap_9","uri":"capability://data.processing.analysis.pii.masking.with.context.preservation","name":"pii-masking-with-context-preservation","description":"Replaces PII with synthetic tokens that preserve certain properties of the original data (e.g., email domain, phone number format, name gender) to maintain context for the LLM while hiding the actual PII. Uses configurable masking strategies (full replacement, partial masking, format-preserving encryption) that balance privacy and utility. Enables the LLM to reason about data types and relationships without accessing sensitive values.","intents":["I want the LLM to understand that a field is an email without revealing the actual email address","I need to preserve the format of PII (e.g., phone numbers still look like phone numbers) so the LLM can process them correctly","I want to hide sensitive data while keeping enough information for the LLM to generate useful responses","I need to mask PII in a way that the LLM can't reverse-engineer the original values"],"best_for":["applications where the LLM needs to understand data types and formats but not actual values","workflows that require balancing privacy with model utility (e.g., customer service chatbots)","use cases where full anonymization loses too much context for the LLM to be useful"],"limitations":["Format-preserving masking (e.g., keeping email domain) may leak information if the LLM can correlate domains with users","Partial masking (e.g., showing first letter of name) reduces privacy — determined attackers may be able to reverse-engineer values","Different masking strategies have different privacy/utility tradeoffs — no single strategy is optimal for all use cases","Rehydration of partially masked data is ambiguous — if the LLM generates a new email with the same domain, it's unclear which original email it corresponds to"],"requires":["Python 3.8+ or Node.js 16+","Optional: cryptography library for format-preserving encryption"],"input_types":["PII values (with type information)","masking strategy (full replacement, partial, format-preserving)","optional: context (e.g., email domain to preserve)"],"output_types":["masked tokens (preserving selected properties)","mapping table (token → original PII)","metadata (which properties were preserved)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 16+","No external API keys required for anonymization itself (zero-trust design)","LLM provider API key (OpenAI, Anthropic, etc.) for actual LLM calls","Anonymization mapping table from the anonymization phase (must be persisted or passed through the same session)","Access to the original anonymized LLM response","Optional: json, xml, csv libraries (usually built-in)","Optional: AST parsing libraries (ast for Python, @babel/parser for JavaScript) for code analysis","Optional: HTML/CSS rendering for web-based visual feedback","API keys for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)","Network access to LLM provider APIs"],"failure_modes":["Pattern-based detection has false positive/negative rates — context-dependent PII (e.g., 'John' as a product name) may be incorrectly flagged","Rehydration assumes deterministic token mapping — if the same PII appears multiple times, it will be replaced with the same token, potentially leaking patterns","No built-in handling of PII in structured data formats (JSON, XML) — requires pre-processing or custom serializers","Performance degrades with very large prompts (>100KB) due to regex scanning overhead","Does not anonymize LLM responses by default — requires explicit configuration to detect PII in model outputs","Rehydration is only as accurate as the anonymization mapping — if anonymization missed PII, rehydration cannot recover it","If the LLM generates new text containing anonymized tokens (e.g., 'The person [PERSON_1] called [PERSON_2]'), rehydration may incorrectly replace them","No built-in handling of PII that appears in different contexts (e.g., same name used as both a person and a product) — requires manual disambiguation","Rehydration performance is O(n) in the number of tokens and mapping entries — large mappings (>10K entries) may add noticeable latency","Format-aware parsing adds complexity — each format (JSON, XML, CSV) requires custom parsing logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=rehydra","compare_url":"https://unfragile.ai/compare?artifact=rehydra"}},"signature":"qN6HcMAqIM5j56cq5J76r39cpeWsu8l0RBZsVdwIbV82YqH1P5Bf5/FFeV6Vnkw/hXoxhMxtJit5dhU4LvceAA==","signedAt":"2026-06-21T15:52:15.945Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rehydra","artifact":"https://unfragile.ai/rehydra","verify":"https://unfragile.ai/api/v1/verify?slug=rehydra","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"}}