{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-memvid--memvid","slug":"memvid--memvid","name":"memvid","type":"agent","url":"https://www.memvid.com","page_url":"https://unfragile.ai/memvid--memvid","categories":["rag-knowledge"],"tags":["ai","context","embedded","faiss","knowledge-base","knowledge-graph","llm","machine-learning","memory","memvid","mv2","nlp","offline-first","opencv","python","rag","retrieval-augmented-generation","semantic-search","vector-database","video-processing"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-memvid--memvid__cap_0","uri":"capability://memory.knowledge.single.file.portable.memory.persistence.with.append.only.smart.frames","name":"single-file portable memory persistence with append-only smart frames","description":"Memvid packages all agent memory—embeddings, search indexes, metadata, and multi-modal content—into a single immutable .mv2 file format with embedded write-ahead logging (WAL) for crash safety. Smart Frames are append-only memory units that are never modified, only added, ensuring durability and portability without external databases. The .mv2 file contains a table-of-contents (TOC), indexed search structures, and a WAL for recovery, enabling agents to carry their entire memory context as a single portable artifact.","intents":["I want to give my AI agent persistent long-term memory without managing a separate vector database or backend infrastructure","I need to package an agent's memory as a single file that can be versioned, backed up, or transferred between environments","I want crash-safe memory writes that guarantee durability even if the process terminates unexpectedly","I need to support offline-first agents that don't require network access to retrieve memories"],"best_for":["solo developers building LLM agents with minimal infrastructure","teams deploying agents to edge devices or resource-constrained environments","applications requiring memory portability across multiple agent instances","systems where memory versioning and reproducibility are critical"],"limitations":["Single-file architecture means concurrent writes from multiple processes require external coordination; no built-in distributed locking","File size grows monotonically (append-only design); requires periodic compaction/rebuild to reclaim space from deleted frames","No native multi-tenant isolation within a single .mv2 file; separate files needed for isolated memory contexts","WAL recovery adds startup latency proportional to uncommitted transaction volume"],"requires":["Rust 1.85.0+ (for building from source) or pre-built binaries via npm/PyPI/Docker","Disk space for .mv2 file (grows with ingested data and embeddings)","Node.js 22+ (for CLI/Node.js SDK) or Python 3.8+ (for Python SDK) or Docker runtime"],"input_types":["text documents","images (via OpenCV processing)","audio (via Whisper integration)","video frames","structured metadata"],"output_types":[".mv2 binary file (portable memory container)","serialized Smart Frame objects","metadata and index snapshots"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_1","uri":"capability://search.retrieval.multi.modal.semantic.search.with.unified.embedding.indexing","name":"multi-modal semantic search with unified embedding indexing","description":"Memvid implements unified semantic search across text, images, audio, and video by storing embeddings in a single index structure within the .mv2 file. The system supports pluggable embedding models (via feature flags like 'vec') and uses FAISS-compatible indexing for fast approximate nearest-neighbor retrieval. All modalities are embedded into a shared vector space, enabling cross-modal queries where a text query can retrieve relevant images or video frames, and vice versa.","intents":["I want to search across mixed-media agent memories (text documents, screenshots, video clips) with a single semantic query","I need fast approximate nearest-neighbor retrieval from embeddings without latency overhead of cloud APIs","I want to retrieve the most contextually relevant memories regardless of whether they're text, image, or video","I need to support agents that reason over heterogeneous data types stored in a single memory index"],"best_for":["multimodal AI agents that process documents, images, and video","applications requiring sub-100ms semantic search latency","teams building agents that need to correlate memories across different content types","offline-first systems where cloud embedding APIs are unavailable"],"limitations":["Embedding quality depends on the underlying model; Memvid does not fine-tune embeddings for domain-specific tasks","FAISS indexing is approximate; recall may degrade with very large indexes (millions of vectors) without careful tuning","Cross-modal search assumes embeddings from different modalities are in the same vector space; misaligned embeddings reduce relevance","No built-in query expansion or semantic reranking; raw similarity scores are returned without learned ranking"],"requires":["Feature flag 'vec' enabled in Rust build or pre-built binary with vector search support","Embedding model (local or API-based) to generate vectors for new content","FAISS library (included in Rust core; optional for SDK wrappers)"],"input_types":["text queries","image queries (as embeddings)","audio queries (as embeddings)","video frame queries (as embeddings)"],"output_types":["ranked list of Smart Frames with similarity scores","metadata and content snippets of retrieved memories","embedding vectors for retrieved items"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_10","uri":"capability://safety.moderation.doctor.and.repair.system.for.corruption.detection.and.recovery","name":"doctor and repair system for corruption detection and recovery","description":"Memvid includes a doctor utility that scans .mv2 files for corruption, inconsistencies, or incomplete transactions. The repair system can fix detected issues by rebuilding indexes, recovering orphaned Smart Frames, or truncating corrupted sections. The doctor operates offline (without requiring a running agent) and provides detailed diagnostics of file health and recovery options.","intents":["I want to detect if my .mv2 file is corrupted or inconsistent","I need to repair a corrupted .mv2 file without losing data","I want to verify that my .mv2 file is healthy before deploying it to production","I need to understand what went wrong with a .mv2 file and how to fix it"],"best_for":["teams managing critical agent memories that must be protected from corruption","systems where .mv2 files are stored on unreliable infrastructure","applications that need to verify memory integrity before deployment","developers debugging memory-related issues in agents"],"limitations":["Doctor cannot fix all types of corruption; severe file header or TOC corruption may be unrecoverable","Repair operations are destructive; they may truncate or discard corrupted data to restore consistency","Doctor does not prevent corruption; it only detects and repairs after the fact","Repair operations require exclusive access to the .mv2 file; cannot run while agent is using the file","No automatic repair; developers must explicitly run doctor and approve repairs"],"requires":["Doctor utility (included in Memvid CLI or SDK)","Exclusive access to .mv2 file (no concurrent agent access)"],"input_types":[".mv2 file path","repair options (e.g., rebuild indexes, truncate corrupted sections)"],"output_types":["corruption diagnostics (detailed report)","repair recommendations","repaired .mv2 file (if repair is applied)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_11","uri":"capability://data.processing.analysis.parallel.ingestion.and.builder.pattern.for.efficient.batch.processing","name":"parallel ingestion and builder pattern for efficient batch processing","description":"Memvid's parallel ingestion system processes multiple documents concurrently using a builder pattern. The builder accepts documents, extracts content in parallel, generates embeddings asynchronously, and batches Smart Frame commits to the .mv2 file. This design decouples I/O (document reading), CPU (embedding generation), and disk (frame writing) operations, maximizing throughput for large-scale ingestion. Errors in individual documents do not block the batch; failed documents are logged and skipped.","intents":["I want to ingest thousands of documents into agent memory as quickly as possible","I need to parallelize document extraction, embedding generation, and indexing","I want to handle ingestion errors gracefully without stopping the entire batch","I need to monitor ingestion progress and handle partial failures"],"best_for":["applications ingesting large document collections (1000+ documents)","systems where ingestion throughput is critical (e.g., daily knowledge base updates)","teams building knowledge bases from diverse sources with varying quality","batch processing pipelines that need to maximize CPU and I/O utilization"],"limitations":["Parallel processing adds complexity; error handling and logging must account for concurrent operations","Memory usage grows with batch size; very large batches (10000+ documents) may exhaust available RAM","Embedding generation is bottlenecked by the embedding model; parallelism cannot exceed model throughput","Batch commits are atomic; if a commit fails, all frames in the batch are rolled back (no partial commits)","No progress checkpointing; if ingestion is interrupted, the entire batch must be restarted"],"requires":["Sufficient RAM for batch processing (proportional to batch size and document size)","Multi-core CPU for parallel processing (single-core systems see no benefit)","Embedding model (local or API-based) with sufficient throughput"],"input_types":["list of documents (files, URLs, or raw content)","batch size configuration","parallel worker count"],"output_types":["ingested Smart Frames (committed to .mv2 file)","ingestion statistics (documents processed, errors, embeddings generated)","error log (failed documents with reasons)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_12","uri":"capability://memory.knowledge.configurable.embedding.model.integration.with.pluggable.providers","name":"configurable embedding model integration with pluggable providers","description":"Memvid supports pluggable embedding models through a provider abstraction layer. Developers can use local embedding models (via ONNX or similar), cloud providers (OpenAI, Anthropic, Hugging Face), or custom models. The system caches embeddings in the .mv2 file to avoid recomputation and supports batch embedding generation for efficiency. Embedding model selection is configurable per ingestion operation, allowing different models for different content types.","intents":["I want to use my preferred embedding model (local, OpenAI, Hugging Face) with Memvid","I need to switch embedding models without re-ingesting all documents","I want to use domain-specific embedding models for better retrieval quality","I need to avoid repeated embedding computation by caching embeddings in the .mv2 file"],"best_for":["teams with specific embedding model preferences (e.g., open-source models for privacy)","applications where embedding quality is critical and domain-specific models are needed","systems where embedding costs are significant and caching is important","developers building agents that need to experiment with different embedding models"],"limitations":["Embedding model selection is per-ingestion; changing models for existing documents requires re-embedding (expensive operation)","Different embedding models produce vectors in different spaces; mixing models in the same index reduces retrieval quality","Local embedding models require sufficient GPU/CPU resources; cloud models incur API costs","Embedding caching increases .mv2 file size (embeddings are typically 1-2KB per document)","No automatic embedding model versioning; developers must track which model was used for each embedding"],"requires":["Embedding model (local or API-based)","API key (for cloud providers like OpenAI or Hugging Face)","Sufficient compute resources (GPU for local models, network for cloud models)"],"input_types":["embedding model configuration (provider, model name, API key)","content to embed (text, images, audio)"],"output_types":["embedding vectors (stored in .mv2 file)","embedding metadata (model name, timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_2","uri":"capability://search.retrieval.full.text.lexical.search.with.inverted.indexing","name":"full-text lexical search with inverted indexing","description":"Memvid provides full-text search via an inverted index (enabled with the 'lex' feature flag) that tokenizes and indexes text content within Smart Frames. The lexical index is stored alongside vector indexes in the .mv2 file and supports boolean queries, phrase matching, and term frequency-based ranking. This complements semantic search for exact-match and keyword-based retrieval scenarios where lexical precision is required.","intents":["I want to retrieve agent memories by exact keyword or phrase match, not just semantic similarity","I need fast full-text search over large document collections without external search engines","I want to combine lexical and semantic search for hybrid retrieval (e.g., keyword filter + semantic ranking)","I need to support agents that reason over specific terminology or domain jargon with exact matching"],"best_for":["agents processing technical documentation or domain-specific text","applications requiring both semantic and keyword-based retrieval","systems where exact phrase matching is critical (e.g., legal, medical)","offline-first agents that cannot rely on external search services"],"limitations":["Inverted index does not support fuzzy matching or typo tolerance; queries must match indexed terms exactly","Index size grows linearly with vocabulary size; very large corpora (millions of unique terms) increase .mv2 file size","Boolean query syntax is limited compared to Elasticsearch or Solr; no advanced query DSL","Tokenization is language-agnostic; specialized tokenizers for non-English languages require custom preprocessing"],"requires":["Feature flag 'lex' enabled in Rust build or pre-built binary with full-text search support","Text content in Smart Frames (raw text or extracted from documents)"],"input_types":["text queries (keywords, phrases, boolean expressions)","document text content"],"output_types":["ranked list of Smart Frames matching query terms","term frequency and relevance scores","matched text snippets with highlighted terms"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_3","uri":"capability://data.processing.analysis.multi.modal.content.ingestion.with.document.extraction.and.frame.processing","name":"multi-modal content ingestion with document extraction and frame processing","description":"Memvid ingests diverse content types (PDFs, images, audio, video) through pluggable document readers and multi-modal processors. PDFs are extracted via the 'pdf_extract' feature, images are processed with OpenCV, audio is transcribed via Whisper integration, and video is decomposed into frames. The parallel ingestion and builder system processes content concurrently, extracting text, generating embeddings, and creating Smart Frames that are atomically committed to the .mv2 file.","intents":["I want to ingest a diverse set of documents (PDFs, images, videos) into agent memory without manual preprocessing","I need to automatically extract text from PDFs and images for indexing and retrieval","I want to transcribe audio and video content so agents can reason over spoken information","I need fast parallel processing of large document batches to populate agent memory efficiently"],"best_for":["agents that need to learn from multi-format document collections","applications ingesting large volumes of PDFs, images, or video","teams building knowledge bases from heterogeneous sources","systems requiring automated content extraction without manual annotation"],"limitations":["PDF extraction quality depends on PDF structure; scanned PDFs without OCR will not extract text (requires separate OCR preprocessing)","Image processing via OpenCV is basic (resizing, normalization); advanced computer vision tasks require separate models","Whisper transcription requires GPU for reasonable latency; CPU-only transcription is slow for large audio/video files","Video frame extraction is naive (fixed interval sampling); no intelligent keyframe detection or scene segmentation","Parallel ingestion adds complexity; errors in one document do not stop the batch, but failed documents are silently skipped unless explicitly logged"],"requires":["Feature flags 'pdf_extract', 'clip', 'whisper' enabled for respective capabilities","OpenCV library (included in Rust core)","Whisper model files (downloaded on first use or pre-cached)","GPU (optional but recommended for Whisper transcription)"],"input_types":["PDF files","image files (PNG, JPEG, etc.)","audio files (MP3, WAV, etc.)","video files (MP4, WebM, etc.)","plain text documents"],"output_types":["extracted text content","embeddings for text and images","transcriptions (audio/video to text)","Smart Frames with metadata (source, type, timestamp)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_4","uri":"capability://memory.knowledge.rag.and.ask.system.with.context.aware.retrieval.and.llm.integration","name":"rag and ask system with context-aware retrieval and llm integration","description":"Memvid's RAG (Retrieval-Augmented Generation) system retrieves relevant Smart Frames based on a query, constructs a context window, and passes it to an LLM for generation. The 'ask' operation combines semantic search, optional lexical filtering, and context ranking to surface the most relevant memories. The system supports configurable context window sizes, ranking strategies, and LLM provider integration (OpenAI, Anthropic, etc.) via standard function-calling APIs.","intents":["I want my agent to answer questions by retrieving relevant memories and augmenting LLM context","I need to control how much context is passed to the LLM (context window size, number of retrieved frames)","I want to rank retrieved memories by relevance before passing them to the LLM","I need to integrate Memvid memory retrieval into existing LLM workflows without rewriting agent logic"],"best_for":["agents that need to answer questions grounded in long-term memory","applications where LLM context is limited and memory must be carefully curated","teams building RAG systems that want to replace complex orchestration with a single memory layer","systems where memory relevance ranking is critical to answer quality"],"limitations":["Context ranking is based on embedding similarity and optional lexical scores; no learned reranking or multi-hop reasoning","Context window size is fixed per query; no dynamic adjustment based on query complexity or LLM capacity","No built-in prompt engineering; developers must format retrieved context into prompts manually","LLM integration is via standard APIs (OpenAI, Anthropic); custom LLM providers require wrapper implementation","No feedback loop to improve retrieval quality based on LLM outputs; ranking is static per query"],"requires":["Populated .mv2 file with indexed memories","LLM API key (OpenAI, Anthropic, or compatible provider)","Network access to LLM provider (or local LLM with compatible API)"],"input_types":["natural language queries","optional lexical filters","context window size parameter","ranking strategy configuration"],"output_types":["ranked list of retrieved Smart Frames","constructed context string","LLM-generated response (if integrated)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_5","uri":"capability://safety.moderation.encryption.and.security.with.optional.data.protection","name":"encryption and security with optional data protection","description":"Memvid supports optional encryption of sensitive data within the .mv2 file using industry-standard cryptographic algorithms. Encryption is applied at the Smart Frame level, allowing selective encryption of sensitive memories while keeping others in plaintext. The system manages encryption keys and provides secure serialization/deserialization of encrypted frames without exposing plaintext to the application layer.","intents":["I want to protect sensitive agent memories (PII, credentials, proprietary data) with encryption","I need to selectively encrypt only sensitive frames while keeping other memories unencrypted","I want to ensure that .mv2 files cannot be read without the correct encryption key","I need to comply with data protection regulations (GDPR, HIPAA) by encrypting sensitive information at rest"],"best_for":["agents handling personally identifiable information (PII) or sensitive data","applications in regulated industries (healthcare, finance, legal)","systems where .mv2 files are stored on untrusted infrastructure","teams that need to share .mv2 files securely across environments"],"limitations":["Encryption is optional per frame; developers must explicitly mark frames for encryption (no automatic detection of sensitive data)","Encryption adds computational overhead (~5-10% latency per encrypted frame access); not suitable for real-time, latency-critical systems","Key management is application-level; Memvid does not provide key rotation, versioning, or centralized key storage","Encrypted frames cannot be indexed for full-text search; lexical queries on encrypted data require decryption first","No support for homomorphic encryption; semantic search on encrypted embeddings requires decryption"],"requires":["Encryption feature enabled in Rust build or pre-built binary","Encryption key (generated and managed by application)","Secure key storage mechanism (e.g., environment variables, key management service)"],"input_types":["plaintext Smart Frame data","encryption key (bytes)"],"output_types":["encrypted Smart Frame data (ciphertext)","decrypted plaintext (with correct key)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_6","uri":"capability://safety.moderation.crash.recovery.and.durability.via.write.ahead.logging","name":"crash recovery and durability via write-ahead logging","description":"Memvid embeds a write-ahead log (WAL) within the .mv2 file to ensure crash safety and durability. All writes (adding Smart Frames, updating indexes) are logged before being applied to the main data structures. In case of process termination or system failure, the WAL is replayed on next open to recover uncommitted transactions and restore the memory to a consistent state. The doctor and repair system can detect and fix corrupted indexes or incomplete transactions.","intents":["I want to guarantee that agent memories are never lost, even if the process crashes during a write","I need to recover from partial writes or corrupted .mv2 files without manual intervention","I want to ensure memory consistency across process restarts","I need to audit what was written to memory and when (via WAL replay)"],"best_for":["long-running agents where process crashes are possible","applications where memory loss is unacceptable (e.g., critical decision-making agents)","systems deployed on unreliable infrastructure (edge devices, mobile)","teams that need to debug memory state changes via WAL replay"],"limitations":["WAL replay adds startup latency proportional to uncommitted transaction volume; very large WALs can slow initialization","WAL is append-only; it grows indefinitely unless periodically compacted (requires explicit compaction operation)","Crash recovery assumes the .mv2 file itself is not corrupted; corruption in the file header or TOC may prevent recovery","Doctor and repair system can fix some corruption but may lose data if corruption is severe","No distributed WAL; single-file design means no replication across multiple machines"],"requires":["Writable filesystem for .mv2 file (WAL requires append operations)","Sufficient disk space for WAL growth (WAL can grow to 10-20% of .mv2 file size before compaction)"],"input_types":["write operations (add Smart Frame, update index)","commit signals"],"output_types":["WAL entries (logged before application)","recovered memory state (on restart)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_7","uri":"capability://tool.use.integration.language.agnostic.multi.sdk.support.with.unified.mv2.interface","name":"language-agnostic multi-sdk support with unified .mv2 interface","description":"Memvid provides native SDKs for Rust, Node.js, and Python, all operating on the same .mv2 file format. The Rust core is the canonical implementation; Node.js and Python SDKs are wrappers around the compiled Rust library. This ensures consistency across languages: a .mv2 file created in Python can be read and modified in Node.js or Rust without format conversion. The CLI provides a command-line interface for shell scripts and automation.","intents":["I want to use Memvid in my preferred programming language (Python, JavaScript, Rust)","I need to share .mv2 files across teams using different languages without format conversion","I want to integrate Memvid into existing Python data science workflows or JavaScript web services","I need a CLI for shell scripts and CI/CD pipelines"],"best_for":["polyglot teams using multiple programming languages","applications where memory must be shared across language boundaries","data science teams using Python that need to integrate with JavaScript web services","DevOps and automation engineers building CI/CD pipelines"],"limitations":["Node.js and Python SDKs are wrappers around Rust; performance is limited by FFI overhead (~5-10% slower than native Rust)","SDK feature parity depends on wrapper implementation; some advanced Rust features may not be exposed in Python/Node.js","Python SDK requires Python 3.8+; older Python versions are not supported","Node.js SDK requires Node.js 22+; older versions lack necessary native module support","CLI is less flexible than SDKs; complex workflows may require SDK usage instead"],"requires":["Python 3.8+ (for Python SDK) or Node.js 22+ (for Node.js SDK) or Rust 1.85.0+ (for Rust API)","Pre-built binaries (via npm, PyPI, cargo) or Docker container"],"input_types":["language-specific API calls (Python, JavaScript, Rust)","CLI commands (shell)"],"output_types":[".mv2 file (same format across all languages)","language-specific objects (Python dicts, JavaScript objects, Rust structs)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_8","uri":"capability://tool.use.integration.feature.gated.optional.capabilities.with.compile.time.configuration","name":"feature-gated optional capabilities with compile-time configuration","description":"Memvid uses Rust feature flags to enable optional capabilities (vec for vector search, lex for full-text search, pdf_extract for PDF processing, clip for image embeddings, whisper for audio transcription). Features are enabled at compile time, reducing binary size and dependencies for users who don't need all capabilities. Pre-built binaries (npm, PyPI, Docker) include commonly-used features; custom builds can select specific features.","intents":["I want a lightweight Memvid binary that only includes the features I need","I want to avoid installing heavy dependencies (Whisper, OpenCV) if I only need text search","I need to customize Memvid for my specific use case (e.g., vector search only, no audio processing)","I want to reduce binary size and startup time by disabling unused features"],"best_for":["developers building minimal agents with limited feature requirements","edge devices or resource-constrained environments where binary size matters","teams that want to avoid dependency bloat for their specific use case","systems where certain capabilities (e.g., Whisper) are not available or desired"],"limitations":["Feature selection is compile-time only; cannot enable/disable features at runtime","Pre-built binaries have a fixed feature set; custom feature combinations require building from source","Building from source requires Rust toolchain and compilation time (~5-10 minutes)","Feature interactions are not always documented; enabling certain feature combinations may have unexpected behavior","Some features have heavy dependencies (Whisper, OpenCV); disabling them saves space but limits functionality"],"requires":["Rust 1.85.0+ and cargo (for custom builds)","Feature flags specified in Cargo.toml (e.g., features = ['vec', 'lex', 'pdf_extract'])"],"input_types":["Cargo.toml configuration","feature flag specifications"],"output_types":["compiled Memvid binary with selected features","reduced binary size (compared to all-features build)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memvid--memvid__cap_9","uri":"capability://automation.workflow.docker.containerization.with.pre.built.cli.image","name":"docker containerization with pre-built cli image","description":"Memvid provides a Docker image (memvid/cli) that packages the Memvid CLI with all dependencies, enabling containerized execution without local installation. The Docker image supports volume mounting for .mv2 files and environment variable configuration for API keys and settings. The image is built with multi-stage compilation to minimize size and includes health checks for container orchestration.","intents":["I want to run Memvid in a containerized environment (Kubernetes, Docker Compose) without installing dependencies","I need to integrate Memvid into CI/CD pipelines with reproducible, isolated execution","I want to avoid dependency conflicts by running Memvid in a container","I need to scale Memvid workloads across multiple containers with shared .mv2 files"],"best_for":["teams using Kubernetes or Docker Compose for orchestration","CI/CD pipelines that need reproducible Memvid execution","applications where dependency isolation is critical","systems requiring horizontal scaling of memory ingestion or retrieval"],"limitations":["Docker adds container overhead (~50-100ms startup latency) compared to native execution","Shared .mv2 files across containers require external coordination; no built-in distributed locking","Volume mounting .mv2 files from network storage (NFS, S3) may have latency and consistency issues","Container image size is larger than minimal binary (~500MB+ with all features); not suitable for serverless functions","No built-in health checks for memory consistency; external monitoring is needed to detect corruption"],"requires":["Docker runtime (Docker Desktop, Docker Engine, or Kubernetes)","Volume mount for .mv2 file (local filesystem or network storage)","Environment variables for API keys and configuration"],"input_types":["CLI commands (passed as container arguments)",".mv2 file (mounted as volume)","environment variables"],"output_types":["modified .mv2 file (persisted to volume)","CLI output (stdout/stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Rust 1.85.0+ (for building from source) or pre-built binaries via npm/PyPI/Docker","Disk space for .mv2 file (grows with ingested data and embeddings)","Node.js 22+ (for CLI/Node.js SDK) or Python 3.8+ (for Python SDK) or Docker runtime","Feature flag 'vec' enabled in Rust build or pre-built binary with vector search support","Embedding model (local or API-based) to generate vectors for new content","FAISS library (included in Rust core; optional for SDK wrappers)","Doctor utility (included in Memvid CLI or SDK)","Exclusive access to .mv2 file (no concurrent agent access)","Sufficient RAM for batch processing (proportional to batch size and document size)","Multi-core CPU for parallel processing (single-core systems see no benefit)"],"failure_modes":["Single-file architecture means concurrent writes from multiple processes require external coordination; no built-in distributed locking","File size grows monotonically (append-only design); requires periodic compaction/rebuild to reclaim space from deleted frames","No native multi-tenant isolation within a single .mv2 file; separate files needed for isolated memory contexts","WAL recovery adds startup latency proportional to uncommitted transaction volume","Embedding quality depends on the underlying model; Memvid does not fine-tune embeddings for domain-specific tasks","FAISS indexing is approximate; recall may degrade with very large indexes (millions of vectors) without careful tuning","Cross-modal search assumes embeddings from different modalities are in the same vector space; misaligned embeddings reduce relevance","No built-in query expansion or semantic reranking; raw similarity scores are returned without learned ranking","Doctor cannot fix all types of corruption; severe file header or TOC corruption may be unrecoverable","Repair operations are destructive; they may truncate or discard corrupted data to restore consistency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7000061519052444,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2026-03-16T13:23:51Z"},"community":{"stars":15337,"forks":1316,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=memvid--memvid","compare_url":"https://unfragile.ai/compare?artifact=memvid--memvid"}},"signature":"3Qm4Vk35lObE639kI2SFbN3Yys+bNgtQIyZGLc0Ce91G33s2ZRM14k+Fv9R56MxGXyIqOZguZpNNmKDotIGXBw==","signedAt":"2026-06-21T20:03:19.918Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/memvid--memvid","artifact":"https://unfragile.ai/memvid--memvid","verify":"https://unfragile.ai/api/v1/verify?slug=memvid--memvid","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"}}