Nowledge Mem

{ "manifest_version": "0.2", "name": "Nowledge-Mem-dxt", "display_name": "Nowledge Mem", "version": "1.0.0", "description": "The graph augmented, smart and local-first personal context manager that just works.", "author": { "name": "Nowledge Labs, LLC", "email": "hello@nowledge-labs.ai", "url": "https://www.nowledge-labs.ai/" }, "homepage": "https://mem.nowledge.co/", "documentation": "https://mem.nowledge.co/docs", "support": "https://mem.nowledge.co/docs/troubleshooting#report-issue", "icon": "icon.png", "screenshots": [ "screenshots/demo1.png", "screenshots/demo2.png", "screenshots/demo3.png", "screenshots/demo4.png" ], "server": { "type": "python", "entry_point": "server/main.py", "mcp_config": { "command": "python3.13", "args": [ "${__dirname}/server/main.py" ], "env": { "DEBUG": "${user_config.debug_mode}", "PYTHONPATH": "${__dirname}/server/lib" } } }, "tools": [ { "name": "memory_search", "description": "Search memories using semantic queries with optional label filtering" }, { "name": "list_memory_labels", "description": "Get all available memory labels with usage counts" }, { "name": "memory_add", "description": "Add new memory to knowledge base" }, { "name": "memory_update", "description": "Update existing memory content, metadata, and labels" } ], "prompts": [ { "name": "sum", "description": "A planning prompt to help analyze content and create high-quality memory entries", "text": "Analyze our current conversation thread and create structured memory entries for important information. Follow this systematic approach:\\n\\n**Analysis Process:**\\n\\n1. **Content Review**: Examine our current conversation thread to identify:\\n - Key insights, decisions, and learnings\\n - Important details, names, and dates\\n - Actionable items and outcomes\\n - Information valuable for future reference\\n\\n2. **Memory Structuring**: For each significant piece of information, I'll create:\\n - **Concise Title** (max 60 characters): Captures the essence and is searchable\\n - **Structured Summary**: Preserves key details, uses clear language, includes actionable items\\n - **Importance Score** (0.1-1.0): Based on significance and future value\\n - **Relevant Labels**: 2-4 labels using lowercase with hyphens if in English (e.g., work, meeting, python, decision)\\n\\n3. **Quality Standards**: Ensure each memory is:\\n - Searchable and useful for future reference\\n - Contains enough context to make sense later\\n - Uses clear, professional language(no emojis, etc.)\\n - Preserves important details while being concise(include needed code examples, etc.)\\n - Focuses on insights and outcomes\\n\\nNow analyze current conversation thread and use the nowledge memory_add tool to create structured memory entries for the important information discussed." } ], "keywords": [ "memory", "state", "progress", "preference" ], "license": "MIT", "repository": { "type": "git", "url": "https://github.com/nowledge-co/claude-dxt" }, "compatibility": { "platforms": [ "darwin" ] } }