{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mempalace--mempalace","slug":"mempalace--mempalace","name":"mempalace","type":"repo","url":"http://mempalaceofficial.com/","page_url":"https://unfragile.ai/mempalace--mempalace","categories":["frameworks-sdks"],"tags":["ai","chromadb","llm","mcp","memory","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mempalace--mempalace__cap_0","uri":"capability://memory.knowledge.spatial.hierarchy.memory.organization.with.palace.metaphor","name":"spatial-hierarchy memory organization with palace metaphor","description":"Organizes persistent AI memory using a five-level spatial hierarchy (Wing → Room → Hall → Tunnel → Drawer) derived from the Method of Loci, enabling structured navigation and metadata filtering beyond flat vector search. Wings represent high-level entities (projects/people), Rooms are topic domains, Halls connect rooms within wings, Tunnels cross-reference related rooms across wings, and Drawers store verbatim text chunks. This metaphorical structure maps directly to ChromaDB vector storage and SQLite knowledge graph, allowing both semantic retrieval and relational fact tracking.","intents":["Organize multi-project conversation history with semantic structure instead of flat lists","Navigate related topics across different projects using cross-wing connections","Filter memory retrieval by project/topic hierarchy before vector search","Maintain spatial mental model of knowledge for better context recall"],"best_for":["AI agents managing long-running multi-project contexts","Teams building persistent memory systems for collaborative LLM workflows","Developers needing structured knowledge organization beyond semantic similarity"],"limitations":["Requires manual or heuristic-based room/wing assignment during ingestion — no automatic hierarchical clustering","Hall and Tunnel connections must be explicitly defined; no automatic cross-reference discovery","Spatial metaphor adds cognitive overhead for teams unfamiliar with Method of Loci"],"requires":["Python 3.9+","ChromaDB instance (local or remote)","SQLite database for knowledge graph"],"input_types":["text (conversation transcripts, code comments)","structured metadata (project names, topic tags)"],"output_types":["hierarchical memory structure (Wing/Room/Drawer objects)","filtered vector search results scoped to hierarchy level","relational triples from knowledge graph"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_1","uri":"capability://memory.knowledge.verbatim.text.storage.with.semantic.indexing","name":"verbatim text storage with semantic indexing","description":"Stores raw, uncompressed conversation and code text chunks (Drawers) in ChromaDB vector store while preserving original formatting and reasoning context. Unlike summarization-based systems that lose critical decision rationale, MemPalace indexes full text with embeddings for semantic retrieval while maintaining the complete original source. Each Drawer is a verbatim chunk with metadata tags (Wing, Room, timestamp, source) enabling both vector similarity search and metadata filtering.","intents":["Retrieve exact original reasoning and context from past conversations without summarization loss","Search across projects using semantic similarity while preserving full source text","Audit decision history by accessing verbatim records with timestamps","Enable LLMs to reference original wording instead of paraphrased summaries"],"best_for":["Teams requiring audit trails and decision provenance","LLM agents needing high-fidelity context for reasoning tasks","Projects where losing nuance in summarization is costly (medical, legal, research)"],"limitations":["Storage overhead grows linearly with conversation volume — no automatic pruning or archival","Verbatim storage includes noise, redundancy, and irrelevant details that summarization would filter","Semantic search quality depends on embedding model quality; poor embeddings reduce retrieval relevance"],"requires":["ChromaDB instance with embedding model (default or custom)","Sufficient disk storage for uncompressed text (estimate 1-10GB per 1M tokens)","Python 3.9+"],"input_types":["text (conversation transcripts, code snippets, documentation)","structured metadata (source, timestamp, author)"],"output_types":["verbatim text chunks with metadata","ranked semantic search results with similarity scores","filtered subsets by metadata tags"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_10","uri":"capability://automation.workflow.cli.interface.for.palace.operations.and.configuration","name":"cli interface for palace operations and configuration","description":"Provides command-line interface (mempalace/cli.py) for all palace operations: initialization, mining, search, memory management, and configuration. CLI supports interactive onboarding flow for first-time setup, guided room/wing assignment during mining, and batch operations for large-scale ingestion. Configuration is stored in YAML/JSON files enabling reproducible palace setups and version control of memory schemas.","intents":["Initialize and configure a new memory palace from the command line","Mine projects and conversations with interactive guidance","Search memory and retrieve records without writing Python code","Manage palace configuration and storage backends via CLI"],"best_for":["Non-technical users setting up memory palaces","Teams using MemPalace as a standalone tool (not embedded in code)","Developers automating palace operations in CI/CD pipelines"],"limitations":["CLI is synchronous; large mining operations block the terminal","Interactive prompts require human input; not suitable for fully automated workflows","Configuration files are YAML/JSON; no GUI for visual configuration","CLI output is text-based; no rich visualization of palace structure"],"requires":["Python 3.9+","MemPalace installed via pip","Terminal/shell access","YAML/JSON configuration files"],"input_types":["CLI arguments (command, options, flags)","interactive prompts (user input)","configuration files (YAML/JSON)"],"output_types":["CLI output (text, tables, JSON)","configuration files (YAML/JSON)","operation logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_11","uri":"capability://data.processing.analysis.benchmark.evaluation.with.longmemeval.scoring","name":"benchmark evaluation with longmemeval scoring","description":"Includes built-in benchmarking suite (tests/test_*.py, benchmarks/) that evaluates memory recall performance using LongMemEval metrics (R@5, R@10, etc.). Benchmarks measure retrieval accuracy on standardized test sets, enabling performance comparison across embedding models, compression levels, and hierarchy configurations. MemPalace achieves 96.6% R@5 on LongMemEval, operating entirely on-device without external APIs.","intents":["Measure memory retrieval accuracy on standardized benchmarks","Compare performance across different embedding models and configurations","Validate that memory system meets recall requirements before deployment","Track performance improvements over time as system evolves"],"best_for":["Teams evaluating memory system quality before production deployment","Researchers benchmarking memory architectures","Developers optimizing embedding models and retrieval strategies"],"limitations":["Benchmarks are synthetic; real-world recall may differ from benchmark scores","LongMemEval metrics (R@5, R@10) don't capture latency or storage overhead","Benchmarks require pre-defined test sets; custom domain benchmarks must be created manually","Benchmark results are sensitive to embedding model quality; changing models requires re-running benchmarks"],"requires":["Python 3.9+","Test data sets (included in repo)","ChromaDB and SQLite instances","Embedding model (default or custom)"],"input_types":["test queries (natural language)","ground truth results (expected Drawer IDs)","configuration parameters (embedding model, hierarchy scope)"],"output_types":["recall metrics (R@5, R@10, MRR, NDCG)","performance reports (latency, throughput)","comparison tables (across configurations)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_12","uri":"capability://memory.knowledge.local.first.architecture.with.zero.external.api.dependencies","name":"local-first architecture with zero external api dependencies","description":"Operates entirely on-device using local ChromaDB and SQLite backends, with no external API calls for embeddings, storage, or inference. Embedding models can be local (e.g., sentence-transformers) or cloud-based (OpenAI, Anthropic), but the system functions without them. This architecture enables offline operation, data privacy (no data leaves the device), and cost efficiency (no per-query API charges).","intents":["Build memory systems that operate offline without internet connectivity","Ensure data privacy by keeping all memory local","Reduce operational costs by avoiding per-query API charges","Deploy memory systems in air-gapped or restricted environments"],"best_for":["Teams with strict data privacy requirements","Organizations in air-gapped or restricted network environments","Projects requiring offline operation or cost optimization","Developers building local-first AI systems"],"limitations":["Local embedding models (sentence-transformers) have lower quality than cloud models (OpenAI, Anthropic)","Requires sufficient local storage for ChromaDB and SQLite (estimate 1-10GB per 1M tokens)","No automatic scaling; single-machine deployment limits concurrency","Maintenance (backups, updates) is manual; no managed service"],"requires":["Python 3.9+","Local ChromaDB instance (or remote, but optional)","Local SQLite database","Sufficient disk space for memory storage","Optional: local embedding model (sentence-transformers) or API key for cloud embeddings"],"input_types":["text (conversations, code, documentation)","configuration (storage paths, embedding model)"],"output_types":["indexed memory records","search results","operation logs"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_13","uri":"capability://data.processing.analysis.multi.platform.chat.export.normalization","name":"multi-platform chat export normalization","description":"Normalizes conversation exports from multiple platforms (Claude, ChatGPT, Slack) into unified internal format via convo_miner.py and normalize.py. Handles variations in speaker identification, timestamp formats, message structure, and metadata across platforms. Normalized conversations are then chunked, embedded, and stored as Drawers with consistent metadata (author, timestamp, source platform).","intents":["Import chat history from Claude, ChatGPT, and Slack in a single operation","Normalize messy transcript formats into consistent memory records","Preserve speaker identity and timestamps across platform variations","Build unified memory from multi-platform conversations"],"best_for":["Teams using multiple chat platforms (Claude, ChatGPT, Slack)","Developers migrating chat history into memory systems","Organizations consolidating conversations from different tools"],"limitations":["Normalization is heuristic-based; edge cases in transcript structure may fail silently","Platform-specific metadata (reactions, threads, mentions) may be lost during normalization","Requires export files in specific formats; no direct API integration with chat platforms","No automatic deduplication; same conversation exported multiple times will be indexed multiple times"],"requires":["Python 3.9+","Chat export files in supported formats (Claude JSON, ChatGPT CSV, Slack exports)","ChromaDB and SQLite instances"],"input_types":["chat export files (JSON, CSV, platform-specific formats)","normalization rules (optional custom rules)"],"output_types":["normalized conversation records","metadata (author, timestamp, source platform)","indexed Drawer records"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_14","uri":"capability://search.retrieval.hierarchical.context.filtering.with.cross.wing.tunnels","name":"hierarchical context filtering with cross-wing tunnels","description":"Enables context retrieval scoped to specific hierarchy levels (Wing, Room, Hall) with optional cross-wing tunnel traversal for related content. Queries can be constrained to a single Wing (project) for focused context, or expanded across Wings via Tunnels (cross-project connections) for broader reasoning. This enables both narrow, focused context retrieval and broad, multi-project reasoning without requiring separate queries.","intents":["Retrieve context focused on a single project (Wing) to avoid irrelevant cross-project noise","Expand context across related projects using Tunnel connections","Navigate memory hierarchy without flattening to vector search","Support both narrow and broad reasoning in single query"],"best_for":["Multi-project teams needing focused and broad context retrieval","LLM agents reasoning across project boundaries","Teams with explicit project relationships (Tunnels)"],"limitations":["Tunnel connections must be manually defined; no automatic cross-project relationship discovery","Hierarchy scoping requires pre-defined Wings/Rooms; ad-hoc topic queries are not supported","Tunnel traversal adds query complexity; no automatic optimization of tunnel paths","Cross-wing queries may return large result sets if many Tunnels are defined"],"requires":["Pre-defined Wings and Rooms","Explicit Tunnel definitions (cross-wing connections)","ChromaDB and SQLite instances","Python 3.9+"],"input_types":["query (text or metadata filters)","scope (Wing, Room, or cross-wing via Tunnels)","tunnel traversal depth (how many hops to follow)"],"output_types":["filtered Drawer results","hierarchy path (Wing → Room → Drawer)","tunnel traversal path (Wing1 → Tunnel → Wing2)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_15","uri":"capability://automation.workflow.configuration.system.with.yaml.json.schemas","name":"configuration system with yaml/json schemas","description":"Manages palace configuration (storage paths, embedding models, entity definitions, room routing rules) via YAML/JSON files with schema validation. Configuration is versioned and can be stored in version control, enabling reproducible palace setups and team collaboration. Supports environment variable substitution for sensitive values (API keys, database paths).","intents":["Version control palace configuration alongside code","Share palace setup across team members","Reproduce palace configuration in different environments (dev, staging, prod)","Manage sensitive configuration (API keys) via environment variables"],"best_for":["Teams collaborating on memory systems","Organizations requiring reproducible configurations","Developers managing multiple palace instances"],"limitations":["Configuration files are static; no dynamic configuration updates without restart","Schema validation is basic; complex configurations may require custom validation","Environment variable substitution is simple string replacement; no advanced templating","No built-in configuration migration; schema changes require manual updates"],"requires":["Python 3.9+","YAML or JSON configuration files","Version control system (Git) for configuration tracking","Environment variables for sensitive values"],"input_types":["YAML/JSON configuration files","environment variables"],"output_types":["validated configuration objects","configuration schema (for documentation)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_16","uri":"capability://tool.use.integration.plugin.manifest.system.for.ai.client.integration","name":"plugin manifest system for ai client integration","description":"Defines plugin manifests that enable AI clients (Claude, custom agents) to discover and integrate MemPalace tools without manual configuration. Manifests specify tool names, parameters, descriptions, and integration points, following MCP standards. This enables one-click integration with compatible AI clients.","intents":["Enable Claude and other AI clients to discover MemPalace tools automatically","Reduce manual configuration overhead for AI client integration","Support multiple AI client types (Claude, custom agents) with single manifest","Enable AI clients to understand tool capabilities and parameters"],"best_for":["Teams integrating MemPalace with Claude or other MCP-compatible clients","Developers building custom AI agents with memory capabilities","Organizations standardizing on MCP for tool integration"],"limitations":["Manifests are static; no dynamic tool discovery based on runtime state","Manifest format is MCP-specific; non-MCP clients require custom integration","No automatic capability negotiation; clients must support all manifest-defined tools","Manifest updates require client restart; no hot-reloading"],"requires":["MCP-compatible AI client","Plugin manifest files (JSON/YAML)","Python 3.9+"],"input_types":["plugin manifest files (JSON/YAML)","tool definitions (name, parameters, description)"],"output_types":["discovered tools (for AI client)","tool parameter schemas (for validation)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_2","uri":"capability://memory.knowledge.dual.backend.semantic.and.relational.storage","name":"dual-backend semantic and relational storage","description":"Implements a dual-storage architecture combining ChromaDB (vector store for semantic text retrieval) and SQLite knowledge graph (for temporal entity-relationship triples). The vector backend indexes Drawer text chunks for similarity search, while the knowledge graph stores Subject-Predicate-Object triples with timestamps to track facts and relationships over time. This separation allows semantic queries ('find discussions about authentication') to coexist with relational queries ('what decisions were made about authentication in Q3?') without forcing either into the other's paradigm.","intents":["Query both semantic similarity and temporal fact relationships in a single system","Track how facts and decisions evolve over time using timestamped triples","Combine vector search results with relational filtering for precise context retrieval","Build knowledge graphs from conversation history without external graph databases"],"best_for":["AI agents needing both semantic understanding and temporal reasoning","Teams tracking decision evolution and fact changes across projects","Developers building local-first memory without external database dependencies"],"limitations":["Dual storage requires synchronization logic — updates to one backend may not reflect in the other without explicit coordination","Knowledge graph triple extraction is heuristic-based; no automatic NLP-driven entity/relation extraction (requires manual definition or plugin)","SQLite knowledge graph scales to ~10M triples before query performance degrades; not suitable for massive knowledge bases"],"requires":["ChromaDB instance (local or remote)","SQLite database (local file or in-memory)","Python 3.9+","Entity/relation definitions for knowledge graph extraction"],"input_types":["text (conversations, code, documentation)","structured triples (Subject, Predicate, Object, timestamp)"],"output_types":["vector search results with similarity scores","temporal entity-relationship triples with timestamps","combined results from both backends"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_3","uri":"capability://data.processing.analysis.project.and.conversation.mining.with.format.normalization","name":"project and conversation mining with format normalization","description":"Automatically ingests data from two sources: local project directories (code, docs, notes via miner.py) and chat exports (Claude, ChatGPT, Slack via convo_miner.py). The system normalizes diverse transcript formats into a unified internal representation using normalize.py, handling variations in speaker identification, timestamps, and message structure. Mined data is then chunked, embedded, and stored in the palace hierarchy with automatic or guided room/wing assignment.","intents":["Index existing codebase and documentation into memory without manual transcription","Import chat history from multiple platforms (Claude, ChatGPT, Slack) in one operation","Normalize messy transcript formats into consistent memory records","Automatically populate palace hierarchy from ingested data"],"best_for":["Teams migrating existing chat history into persistent memory","Developers onboarding large codebases into AI memory systems","Multi-platform teams using different chat tools (Claude, ChatGPT, Slack)"],"limitations":["Project mining requires local filesystem access — no support for remote repos (GitHub, GitLab) without cloning first","Conversation format normalization is heuristic-based; edge cases in transcript structure may fail silently or produce malformed records","No automatic deduplication during mining — duplicate messages/code blocks are indexed multiple times","Room/wing assignment is guided (interactive prompts) or heuristic-based; no ML-driven hierarchical clustering"],"requires":["Python 3.9+","Local filesystem access for project mining","Chat export files in supported formats (JSON, CSV, or platform-specific formats)","ChromaDB and SQLite instances configured"],"input_types":["local directory paths (for code/docs)","chat export files (Claude JSON, ChatGPT CSV, Slack exports)","text files (markdown, code, plain text)"],"output_types":["normalized conversation records with metadata","indexed code chunks with file paths and line numbers","palace hierarchy assignments (Wing/Room/Drawer)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_4","uri":"capability://tool.use.integration.mcp.server.with.19.specialized.tools.for.memory.operations","name":"mcp server with 19 specialized tools for memory operations","description":"Exposes MemPalace functionality as a Model Context Protocol (MCP) server with 19 specialized tools for search, retrieval, storage, and maintenance operations. Tools are organized by function (search_palace, retrieve_drawer, add_memory, etc.) and accept structured parameters with validation. The MCP server enables any MCP-compatible AI client (Claude, custom agents) to interact with the memory system without direct Python imports, supporting both synchronous and asynchronous tool invocation.","intents":["Integrate MemPalace memory into Claude or other MCP-compatible AI clients","Build custom AI agents that can search and store memories during reasoning","Expose memory operations via standardized MCP protocol without custom API wrappers","Enable multi-agent systems to share a common memory backend"],"best_for":["Teams building Claude-integrated agents with persistent memory","Developers creating custom MCP servers that need memory capabilities","Multi-agent systems requiring shared memory backend"],"limitations":["MCP server adds ~50-200ms latency per tool call due to serialization and IPC overhead","Tool parameter validation is schema-based; complex nested queries require careful JSON structuring","No built-in rate limiting or quota management — memory operations are unbounded","Asynchronous tool invocation requires MCP client support; some clients may only support synchronous calls"],"requires":["Python 3.9+","MCP-compatible AI client (Claude, custom agent framework)","Configured ChromaDB and SQLite backends","MCP server running (via CLI or Python process)"],"input_types":["MCP tool calls with JSON parameters","search queries (text, metadata filters)","memory records (text, metadata)"],"output_types":["MCP tool results (JSON-serialized)","search results with metadata","operation status (success/failure with details)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_5","uri":"capability://search.retrieval.semantic.search.with.metadata.filtering.and.hierarchy.scoping","name":"semantic search with metadata filtering and hierarchy scoping","description":"Performs vector similarity search on Drawer text chunks using ChromaDB embeddings, with optional metadata filtering (Wing, Room, timestamp, source) and hierarchy scoping. Search can be constrained to a specific Wing (project) or Room (topic) before vector search, reducing irrelevant results. Results are ranked by cosine similarity and returned with full metadata, enabling LLMs to understand context (project, topic, time period) alongside semantic relevance.","intents":["Find relevant past discussions or code snippets using natural language queries","Search within a specific project (Wing) or topic (Room) to reduce noise","Retrieve context with full metadata (source, timestamp, author) for citation","Combine semantic search with temporal filtering (e.g., 'decisions made in Q3')"],"best_for":["LLM agents needing context retrieval during reasoning","Teams searching multi-project memory with topic scoping","Developers building RAG systems with hierarchical context"],"limitations":["Search quality depends on embedding model quality; poor embeddings produce low-relevance results","Metadata filtering is exact-match only; no fuzzy matching or partial string search","Hierarchy scoping requires pre-defined Wings/Rooms; ad-hoc topic queries are not supported","No full-text search fallback; if embeddings fail, no alternative retrieval method"],"requires":["ChromaDB instance with embedding model","Indexed Drawer records with metadata","Python 3.9+"],"input_types":["natural language query (text)","metadata filters (Wing, Room, timestamp range)","hierarchy scope (optional Wing/Room ID)"],"output_types":["ranked list of Drawer results with similarity scores","full metadata (source, timestamp, hierarchy path)","original verbatim text"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_6","uri":"capability://memory.knowledge.knowledge.graph.temporal.entity.relationship.tracking","name":"knowledge graph temporal entity-relationship tracking","description":"Stores and queries Subject-Predicate-Object triples with timestamps in SQLite knowledge graph, enabling temporal reasoning about facts and relationships. Triples are extracted from ingested text (via heuristic or manual definition) and stored with creation/modification timestamps. Supports temporal queries ('what was the state of X on date Y?') and relationship traversal ('what entities are connected to X?'). The knowledge graph operates independently from vector search, allowing relational reasoning without semantic similarity.","intents":["Track how facts and decisions evolve over time (e.g., 'architecture decisions in Q2 vs Q3')","Query relationships between entities without semantic similarity (e.g., 'who reported to whom?')","Build knowledge graphs from conversation history without external graph databases","Audit decision provenance by tracing entity relationships over time"],"best_for":["Teams tracking decision evolution and organizational changes","Projects requiring temporal reasoning (e.g., 'what was the plan before the pivot?')","Developers building local-first knowledge graphs without external dependencies"],"limitations":["Triple extraction is heuristic-based or manual; no automatic NLP-driven entity/relation extraction","Temporal queries require explicit timestamp filtering; no automatic time-series reasoning","SQLite knowledge graph scales to ~10M triples; performance degrades beyond that","No automatic schema inference; entity types and relations must be pre-defined"],"requires":["SQLite database (local or in-memory)","Entity/relation definitions for triple extraction","Python 3.9+","Timestamped input data"],"input_types":["Subject-Predicate-Object triples","timestamps (creation/modification)","entity type definitions"],"output_types":["temporal triples with timestamps","relationship graphs (entity → entity connections)","temporal snapshots (state at specific time)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_7","uri":"capability://data.processing.analysis.aaak.dialect.compression.and.auto.save.hooks","name":"aaak dialect compression and auto-save hooks","description":"Implements AAAK (Adaptive Archival Annotation Kompression) dialect for compressing memory records with configurable compression levels and auto-save hooks that trigger on file changes. The dialect supports lossless compression of verbatim text while preserving metadata, reducing storage overhead. Auto-save hooks (shell scripts or Python CLI) monitor project directories and automatically ingest changes into the palace, enabling continuous memory updates without manual mining.","intents":["Reduce storage overhead for large memory palaces through compression","Automatically sync project changes into memory without manual re-mining","Archive old memory records with compression while keeping recent records uncompressed","Monitor code/documentation changes and update memory in real-time"],"best_for":["Teams with large memory palaces (>1GB) needing storage optimization","Projects with frequent code/documentation changes requiring continuous memory sync","Developers building auto-updating memory systems"],"limitations":["AAAK compression is lossless but adds CPU overhead during compression/decompression (~10-50ms per record)","Auto-save hooks require filesystem monitoring; may miss changes if system is offline","Hook scripts are shell or Python; no support for other languages","Compression effectiveness varies by content type (code compresses better than natural language)"],"requires":["Python 3.9+","Filesystem access for auto-save hooks","ChromaDB and SQLite instances","Shell or Python environment for hook scripts"],"input_types":["verbatim text records","compression level settings (0-9)","file paths for auto-save monitoring"],"output_types":["compressed memory records","compression statistics (original size, compressed size, ratio)","auto-save event logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_8","uri":"capability://automation.workflow.deduplication.and.database.repair.operations","name":"deduplication and database repair operations","description":"Provides maintenance tools for identifying and removing duplicate memory records (exact text matches, near-duplicates via embedding similarity), repairing corrupted records, and migrating data between storage backends. Deduplication operates on both vector store (ChromaDB) and knowledge graph (SQLite), identifying duplicates via text hashing and embedding similarity. Repair operations validate record integrity, fix malformed metadata, and rebuild indexes.","intents":["Remove duplicate memories from ingested data (same conversation indexed multiple times)","Identify near-duplicate content using embedding similarity","Repair corrupted records or metadata inconsistencies","Migrate memory data between ChromaDB/SQLite versions or backends"],"best_for":["Teams maintaining large memory palaces with accumulated duplicates","Developers recovering from data corruption or ingestion errors","Teams migrating between storage backends or versions"],"limitations":["Deduplication is time-consuming for large palaces (>100GB); may require hours to complete","Near-duplicate detection via embedding similarity is heuristic; threshold tuning is required","Repair operations are destructive; no automatic rollback if repair fails","Migration operations require both source and destination backends to be available simultaneously"],"requires":["Python 3.9+","ChromaDB and SQLite instances","Sufficient disk space for temporary dedup/repair operations","Database backups before running repair"],"input_types":["memory palace (ChromaDB + SQLite)","dedup parameters (similarity threshold, hash algorithm)","repair scope (specific records or entire palace)"],"output_types":["dedup report (duplicates found, removed)","repair report (corrupted records fixed)","migration log (records migrated, errors)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mempalace--mempalace__cap_9","uri":"capability://data.processing.analysis.entity.detection.and.registry.with.room.routing","name":"entity detection and registry with room routing","description":"Automatically detects entities (people, projects, concepts) in ingested text and maintains a registry mapping entities to palace locations (Wings/Rooms). Room detection and routing heuristics assign new entities to appropriate Wings/Rooms based on context and existing entity mappings. 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