{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-memgpt","slug":"pypi-memgpt","name":"memgpt","type":"repo","url":"https://pypi.org/project/memgpt/","page_url":"https://unfragile.ai/pypi-memgpt","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-memgpt__cap_0","uri":"capability://code.generation.editing.memory.augmented.language.model.training.on.domain.specific.data","name":"memory-augmented language model training on domain-specific data","description":"Trains GPT models with external memory mechanisms using patient data as the training corpus. Implements memory-augmented architectures that allow the model to store, retrieve, and update contextual information across conversation turns, enabling persistent state management beyond standard transformer context windows. Uses domain-specific fine-tuning on healthcare data to specialize the base model for medical reasoning tasks.","intents":["Train a conversational AI that remembers patient history and context across multiple sessions","Build a medical assistant that can reference and update patient information dynamically","Create domain-specialized language models that maintain state without retraining"],"best_for":["Healthcare AI teams building patient-facing conversational systems","Researchers developing memory-augmented transformer architectures","Organizations needing persistent context in multi-turn medical dialogues"],"limitations":["Requires substantial patient data for effective fine-tuning; limited by data privacy regulations (HIPAA compliance not guaranteed)","Memory retrieval adds latency to inference; no built-in optimization for real-time clinical use","Training computational cost scales with model size and dataset volume; requires GPU infrastructure","No pre-trained checkpoints provided; full training pipeline must be implemented by users"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.6+","GPU with minimum 16GB VRAM for model training","Patient dataset in structured format (CSV, JSON, or database)","Transformers library 4.0+"],"input_types":["structured patient data (demographics, medical history, lab results)","unstructured clinical notes (text)","conversation transcripts for fine-tuning"],"output_types":["trained model checkpoint","memory-augmented embeddings","conversation responses with cited memory references"],"categories":["code-generation-editing","memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_1","uri":"capability://data.processing.analysis.patient.data.preprocessing.and.vectorization.for.memory.storage","name":"patient data preprocessing and vectorization for memory storage","description":"Transforms raw patient data (structured records, clinical notes, lab results) into embeddings and indexed memory representations suitable for retrieval during inference. Implements ETL pipeline that handles data normalization, tokenization, and conversion to vector format for semantic search. Likely uses embedding models to create dense representations of patient information for efficient memory lookup.","intents":["Convert unstructured clinical notes into searchable memory embeddings","Normalize and structure diverse patient data sources for consistent model input","Create indexed patient knowledge bases for fast retrieval during conversation"],"best_for":["Data engineers preparing healthcare datasets for AI training","Teams migrating from traditional EHR systems to AI-augmented workflows","Researchers building memory-indexed medical knowledge bases"],"limitations":["No built-in de-identification or HIPAA compliance; requires external PII masking","Embedding quality depends on pre-trained model choice; no fine-tuned medical embeddings provided","Batch processing only; no streaming ingestion for real-time data updates","Vector index scalability not documented; unclear performance with >1M patient records"],"requires":["Python 3.8+","Pandas or Polars for data manipulation","Embedding model (OpenAI, Sentence-Transformers, or custom)","Vector database client (FAISS, Pinecone, or Weaviate) for index storage","Structured patient data in CSV, JSON, or database format"],"input_types":["CSV/JSON patient records","unstructured clinical text","structured lab results and vital signs","medical ontology mappings (ICD-10, SNOMED-CT)"],"output_types":["vector embeddings (dense float arrays)","indexed memory store (FAISS index or vector DB records)","normalized patient metadata for retrieval"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_2","uri":"capability://memory.knowledge.multi.turn.conversation.state.management.with.persistent.memory","name":"multi-turn conversation state management with persistent memory","description":"Manages conversation state across multiple dialogue turns by maintaining and updating an external memory store that persists patient context, previous interactions, and learned information. Implements memory read/write operations integrated into the conversation loop, allowing the model to retrieve relevant patient history before generating responses and update memory with new information from each turn. Architecture likely uses a memory controller that decides what to store, retrieve, and forget.","intents":["Build chatbots that remember patient information across sessions without retraining","Implement conversation systems that can reference and update patient context dynamically","Create multi-turn dialogues where the model learns and adapts to individual patient profiles"],"best_for":["Healthcare providers building persistent patient AI assistants","Conversational AI teams needing stateful dialogue without session resets","Medical chatbot developers requiring context continuity across days/weeks"],"limitations":["Memory update conflicts not handled; no built-in versioning or conflict resolution for concurrent updates","Memory retrieval adds 50-200ms per turn depending on index size; not optimized for sub-100ms latency","No automatic memory pruning or forgetting; unbounded memory growth over time","Requires external state store (database, vector DB); no built-in persistence layer"],"requires":["Python 3.8+","Persistent storage backend (PostgreSQL, MongoDB, or vector DB like Pinecone)","Embedding model for memory retrieval","Conversation framework (likely custom or built on Langchain/LlamaIndex)","Memory controller implementation (custom or provided by package)"],"input_types":["user message (text)","patient ID or session identifier","memory query parameters"],"output_types":["retrieved memory context (text or embeddings)","model response with memory citations","memory update operations (insert/update/delete)"],"categories":["memory-knowledge","planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_3","uri":"capability://code.generation.editing.healthcare.specific.model.fine.tuning.with.clinical.evaluation.metrics","name":"healthcare-specific model fine-tuning with clinical evaluation metrics","description":"Provides fine-tuning pipeline optimized for medical language models with evaluation metrics specific to clinical accuracy, safety, and relevance. Implements training loops that use domain-specific loss functions and evaluation criteria (e.g., clinical correctness, adherence to medical guidelines, safety constraints). Likely includes validation against medical knowledge bases and human expert feedback integration.","intents":["Fine-tune base language models on proprietary patient data while maintaining medical accuracy","Evaluate model outputs against clinical standards and safety guidelines during training","Adapt pre-trained models to specific medical specialties or institutional protocols"],"best_for":["Healthcare institutions fine-tuning models on internal patient data","Medical AI teams requiring clinical validation during training","Researchers developing specialized medical language models"],"limitations":["No built-in regulatory compliance checking; HIPAA/FDA validation responsibility on user","Clinical evaluation metrics require expert annotation; no automated ground truth generation","Fine-tuning convergence unpredictable with small medical datasets (<10K examples)","Computational cost high; requires multi-GPU setup for reasonable training time"],"requires":["Python 3.8+","PyTorch or TensorFlow with distributed training support","GPU cluster (minimum 2x 16GB GPUs recommended)","Annotated medical dataset with clinical labels","Medical knowledge base or guideline documents for validation","Transformers library 4.0+"],"input_types":["base model checkpoint (HuggingFace format)","annotated patient data with clinical labels","medical guidelines or knowledge base","expert feedback or preference data"],"output_types":["fine-tuned model checkpoint","training metrics (loss, clinical accuracy, safety scores)","evaluation report with clinical validation results"],"categories":["code-generation-editing","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_4","uri":"capability://text.generation.language.memory.augmented.inference.with.context.retrieval.and.generation","name":"memory-augmented inference with context retrieval and generation","description":"Executes inference by retrieving relevant patient memory before generating responses, combining retrieved context with the current query to produce medically-informed outputs. Implements a retrieval-then-generate pipeline where memory lookup happens before decoding, allowing the model to condition responses on patient history. Architecture likely uses attention mechanisms to weight retrieved memory against current input.","intents":["Generate patient-specific responses that reference and incorporate historical context","Retrieve relevant patient information automatically before generating medical advice","Produce outputs that cite memory sources and maintain consistency with patient history"],"best_for":["Healthcare chatbot developers building context-aware patient interactions","Medical AI teams needing explainable responses with memory citations","Clinical decision support systems requiring patient history integration"],"limitations":["Retrieval latency adds 50-200ms per inference; not suitable for <100ms response requirements","Retrieved context quality depends on embedding model; no guarantee of clinically relevant results","Memory hallucination possible if retrieval returns outdated or conflicting information","No built-in conflict resolution between retrieved memory and model knowledge"],"requires":["Python 3.8+","Trained memory-augmented model checkpoint","Vector index of patient memory (FAISS, Pinecone, or similar)","Embedding model for memory retrieval","Inference framework (PyTorch, ONNX, or TensorFlow)"],"input_types":["user query (text)","patient ID or session context","optional memory retrieval parameters"],"output_types":["generated response (text)","retrieved memory context (text with relevance scores)","memory citations or references"],"categories":["text-generation-language","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_5","uri":"capability://automation.workflow.batch.inference.on.patient.cohorts.with.memory.initialization","name":"batch inference on patient cohorts with memory initialization","description":"Processes multiple patients in batch mode, initializing and managing separate memory states for each patient while generating responses. Implements batched inference that maintains per-patient memory isolation, allowing efficient processing of patient cohorts while preserving individual context. Likely uses memory pooling or per-patient memory indices to handle batch operations.","intents":["Generate responses for multiple patients simultaneously while maintaining separate memory contexts","Process patient cohorts efficiently without losing individual patient history","Initialize memory states for new patients in bulk operations"],"best_for":["Healthcare systems processing large patient populations","Batch analysis pipelines generating personalized medical insights","Research teams evaluating model performance across patient cohorts"],"limitations":["Memory isolation overhead increases with batch size; no documented scaling limits","Batch processing requires all patients to have compatible memory schemas","No dynamic batching; fixed batch size required at inference time","Memory index lookups scale linearly with batch size; potential bottleneck for large cohorts"],"requires":["Python 3.8+","Trained memory-augmented model","Per-patient memory indices or centralized memory store with patient partitioning","Batch inference framework (PyTorch DataLoader or custom batching)","Sufficient GPU memory for batch size (scales with model size and batch count)"],"input_types":["batch of patient IDs","batch of queries or prompts","patient metadata for memory initialization"],"output_types":["batch of generated responses","per-patient memory states (updated)","batch inference metrics"],"categories":["automation-workflow","data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_6","uri":"capability://memory.knowledge.memory.update.and.consolidation.with.conflict.resolution","name":"memory update and consolidation with conflict resolution","description":"Updates patient memory with new information from conversations and consolidates memory entries to prevent redundancy and conflicts. Implements memory write operations that handle duplicate detection, temporal ordering, and conflict resolution when new information contradicts stored memory. Likely uses heuristics or learned policies to decide which information to keep, update, or discard.","intents":["Update patient memory with new information learned during conversations","Consolidate redundant or conflicting memory entries to maintain consistency","Manage memory growth and prevent unbounded storage of duplicate information"],"best_for":["Long-running patient AI assistants requiring memory maintenance","Healthcare systems managing evolving patient information over time","Teams building memory-augmented systems with strict consistency requirements"],"limitations":["Conflict resolution heuristics may not handle complex medical contradictions (e.g., conflicting diagnoses)","No built-in temporal reasoning; cannot handle time-dependent medical information (e.g., medication changes)","Memory consolidation is lossy; important nuances may be discarded during deduplication","No rollback or audit trail for memory updates; difficult to debug memory corruption"],"requires":["Python 3.8+","Persistent memory store with update/delete operations","Embedding model for similarity detection","Conflict resolution policy (rule-based or learned)","Optional: temporal database for versioning"],"input_types":["new information to store (text)","patient ID or memory context","optional: confidence scores or metadata"],"output_types":["memory update confirmation","conflict resolution decisions","consolidated memory state"],"categories":["memory-knowledge","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_7","uri":"capability://memory.knowledge.medical.knowledge.base.integration.for.memory.grounding","name":"medical knowledge base integration for memory grounding","description":"Grounds patient memory and model outputs against external medical knowledge bases (e.g., medical ontologies, clinical guidelines, drug databases) to ensure consistency and accuracy. Implements knowledge lookup and validation that checks patient information against authoritative medical sources, flagging inconsistencies or outdated information. Likely uses SNOMED-CT, ICD-10, or similar medical coding systems for normalization.","intents":["Validate patient information against medical knowledge bases to catch inconsistencies","Normalize medical terminology in patient memory using standard ontologies","Ground model outputs in evidence-based medical guidelines and best practices"],"best_for":["Healthcare systems requiring evidence-based AI outputs","Medical AI teams building safety-critical applications","Researchers integrating structured medical knowledge with language models"],"limitations":["Knowledge base coverage incomplete; rare conditions or new medications may not be indexed","Ontology mapping is lossy; clinical nuances may be lost in standardized coding","Knowledge base updates lag clinical practice; may reference outdated guidelines","Integration overhead adds latency; knowledge lookup can add 100-500ms per query"],"requires":["Python 3.8+","Medical knowledge base access (SNOMED-CT, ICD-10, RxNorm, or similar)","Knowledge base client library or API","Ontology mapping layer for normalization","Optional: local knowledge base mirror for low-latency access"],"input_types":["patient information (diagnoses, medications, symptoms)","model outputs for validation","medical terminology for normalization"],"output_types":["normalized medical codes (ICD-10, SNOMED-CT)","validation results (consistent/inconsistent)","grounded outputs with knowledge base citations"],"categories":["memory-knowledge","safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_8","uri":"capability://safety.moderation.privacy.preserving.memory.storage.with.optional.de.identification","name":"privacy-preserving memory storage with optional de-identification","description":"Provides mechanisms for storing patient memory while protecting sensitive information through de-identification, encryption, or differential privacy techniques. Implements privacy controls that can mask PII (names, dates, identifiers) while preserving clinically relevant information for memory retrieval. Likely supports configurable privacy policies and optional encryption at rest.","intents":["Store patient memory securely without exposing sensitive personally identifiable information","Enable memory-augmented AI while maintaining HIPAA or GDPR compliance","Implement privacy-preserving memory that can be shared for research without revealing patient identity"],"best_for":["Healthcare organizations handling sensitive patient data","Research teams building privacy-compliant medical AI systems","Teams subject to HIPAA, GDPR, or other data protection regulations"],"limitations":["De-identification may remove clinically important temporal information (dates)","Privacy-preserving techniques add computational overhead; retrieval latency increases 20-50%","No built-in regulatory compliance guarantee; users responsible for validation","Encryption at rest requires key management infrastructure; no built-in key rotation"],"requires":["Python 3.8+","De-identification library (e.g., Presidio, PhiBERT) or custom PII masking","Encryption library (cryptography, PyCryptodome) for optional encryption","Privacy-preserving database or encrypted storage backend","Optional: differential privacy library (OpenDP, PyDP)"],"input_types":["raw patient data with PII","privacy policy configuration","optional: differential privacy parameters"],"output_types":["de-identified patient memory","encrypted memory store","privacy audit logs"],"categories":["safety-moderation","memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-memgpt__cap_9","uri":"capability://data.processing.analysis.model.evaluation.and.benchmarking.on.medical.tasks","name":"model evaluation and benchmarking on medical tasks","description":"Provides evaluation framework for assessing memory-augmented model performance on medical tasks using domain-specific benchmarks and metrics. Implements evaluation pipelines that measure clinical accuracy, safety, coherence, and memory effectiveness using medical datasets and expert annotations. Likely includes comparison against baseline models and ablation studies.","intents":["Evaluate memory-augmented models on medical reasoning tasks with clinical metrics","Benchmark model performance against baselines to quantify memory contribution","Identify model weaknesses and failure modes in medical contexts"],"best_for":["Researchers developing and comparing medical language models","Healthcare teams validating AI systems before clinical deployment","ML engineers optimizing model architectures for medical tasks"],"limitations":["Evaluation metrics require expert annotation; no automated ground truth for clinical correctness","Benchmark datasets may not represent target patient population; generalization unclear","Evaluation is computationally expensive; full evaluation can take hours/days","No standardized medical benchmarks; custom evaluation required for domain-specific tasks"],"requires":["Python 3.8+","Trained model checkpoint","Annotated medical evaluation dataset","Evaluation metrics library (custom or standard NLP metrics)","Optional: medical expert reviewers for human evaluation"],"input_types":["model checkpoint","evaluation dataset (medical questions, patient scenarios)","reference answers or expert annotations","optional: human evaluation rubrics"],"output_types":["evaluation metrics (accuracy, F1, clinical correctness scores)","performance comparison vs baselines","error analysis and failure case documentation"],"categories":["data-processing-analysis","planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.6+","GPU with minimum 16GB VRAM for model training","Patient dataset in structured format (CSV, JSON, or database)","Transformers library 4.0+","Pandas or Polars for data manipulation","Embedding model (OpenAI, Sentence-Transformers, or custom)","Vector database client (FAISS, Pinecone, or Weaviate) for index storage","Structured patient data in CSV, JSON, or database format","Persistent storage backend (PostgreSQL, MongoDB, or vector DB like Pinecone)"],"failure_modes":["Requires substantial patient data for effective fine-tuning; limited by data privacy regulations (HIPAA compliance not guaranteed)","Memory retrieval adds latency to inference; no built-in optimization for real-time clinical use","Training computational cost scales with model size and dataset volume; requires GPU infrastructure","No pre-trained checkpoints provided; full training pipeline must be implemented by users","No built-in de-identification or HIPAA compliance; requires external PII masking","Embedding quality depends on pre-trained model choice; no fine-tuned medical embeddings provided","Batch processing only; no streaming ingestion for real-time data updates","Vector index scalability not documented; unclear performance with >1M patient records","Memory update conflicts not handled; no built-in versioning or conflict resolution for concurrent updates","Memory retrieval adds 50-200ms per turn depending on index size; not optimized for sub-100ms latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","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-memgpt","compare_url":"https://unfragile.ai/compare?artifact=pypi-memgpt"}},"signature":"lAVJqjJLr/yhlJq1tR8rwIM+uRQwIONrJ/k23JoSIQvK/S3zq/XFmAZrCTN9HHhhNjtOdWnk2D8ulyawH+zQCQ==","signedAt":"2026-06-21T11:19:47.848Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-memgpt","artifact":"https://unfragile.ai/pypi-memgpt","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-memgpt","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"}}