io.github.purmemo-ai/purmemo

CROSS-PLATFORM DISCOVERY: Find related conversations across ALL AI platforms. Uses Purmemo's semantic clustering to automatically discover conversations about similar topics, regardless of which AI platform was used (ChatGPT, Claude Desktop, Gemini, etc). WHAT THIS DOES: - Searches for memories matching your query - Uses AI-organized semantic clusters to find related conversations - Groups results by topic cluster with platform indicators - Shows conversations you may have forgotten about on other platforms EXAMPLES: User: "Show me all conversations about the marketing project" → Finds conversations across ChatGPT, Claude, Gemini automatically User: "What have I discussed about licensing requirements?" → Discovers related discussions from all platforms, grouped by semantic similarity User: "Find everything about React hooks" → Returns conversations from any platform where you discussed React hooks RESPONSE FORMAT: Shows memories grouped by semantic cluster with platform badges (ChatGPT, Claude, Gemini) Each cluster represents conversations about similar topics across all platforms

Get complete details of a specific memory, including all linked parts if chunked

Get the current user's cognitive identity and active session context. Call this at the START of a conversation to understand who you're talking to — their role, expertise, current project, and recent memory themes. This is the core of Purmemo's identity layer: once set in the dashboard, your identity travels silently to every AI session so you're never explaining yourself from scratch again. WHAT IT RETURNS: - identity: role, expertise areas, primary domain, work style, preferred tools - current_session: what the user is working on right now (project, focus) - memory_summary: 2-3 sentence synthesis of the user's most recent memory themes WHEN TO CALL: - At the start of every new session (add to Claude system prompt) - When user says "load my context" or "what do you know about me?" - Before making recommendations that depend on knowing the user's background EXAMPLE USAGE: → User starts new Claude session → Claude calls get_user_context automatically → Response: { role: "founder", expertise: ["product", "fullstack"], project: "purmemo", focus: "identity layer", memory_summary: "Chris has been building Purmemo's..." } → Claude responds with full context already loaded — no re-explaining needed

Search and retrieve saved memories with intelligent semantic ranking. 🎯 BASIC SEARCH: recall_memories(query="authentication") → Returns all memories about authentication, ranked by semantic relevance 🔍 FILTERED SEARCH (Phase 2 Knowledge Graph Intelligence): Use filters when you need PRECISION over semantic similarity: ✓ entity="name" - Find memories mentioning specific people/projects/technologies Example: entity="purmemo" → Only memories discussing purmemo ✓ has_observations=true - Find substantial, fact-dense conversations Example: has_observations=true → Only high-quality technical discussions ✓ initiative="project" - Scope to specific initiatives/goals Example: initiative="Q1 OKRs" → Only Q1-related memories ✓ intent="type" - Filter by conversation purpose Options: decision, learning, question, blocker Example: intent="blocker" → Only conversations about blockers 💡 WHEN TO FILTER: - Use entity when user asks about specific person/project by name - Use has_observations for "detailed" or "substantial" requests - Use initiative/stakeholder for project-specific searches - Use intent when user asks for decisions, learnings, or blockers 📝 COMBINED EXAMPLES: recall_memories(query="auth", entity="purmemo", has_observations=true) → Find detailed technical discussions about purmemo authentication recall_memories(query="blockers", intent="blocker", stakeholder="Engineering") → Find engineering team blockers

Save complete conversations as living documents. REQUIRED: Send COMPLETE conversation in 'conversationContent' parameter (minimum 100 chars, should be thousands). Include EVERY message verbatim - NO summaries or partial content. Intelligently tracks context, extracts project details, and maintains a single memory per conversation topic. LIVING DOCUMENT + INTELLIGENT PROJECT TRACKING: - Each conversation becomes a living document that grows over time - Automatically extracts project context (name, component, feature being discussed) - Detects work iteration and status (planning/in_progress/completed/blocked) - Generates smart titles like "Purmemo - Timeline View - Implementation" (no more timestamp titles!) - Tracks technologies, tools used, and identifies relationships/dependencies - Works like Chrome extension: intelligent memory that grows with each save How memory updating works: - Conversation ID auto-generated from title (e.g., "MCP Tools" → "mcp-tools") - Same title → UPDATES existing memory (not create duplicate) - "Save progress" → Updates most recent memory for current project context - Explicit conversationId → Always updates that specific memory - Example: Saving "Project X Planning" three times = ONE memory updated three times - To force new memory: Change title or use different conversationId SERVER AUTO-CHUNKING: - Large conversations (>15K chars) automatically split into linked chunks - Small conversations (<15K chars) saved directly as single memory - You always send complete content - server handles chunking intelligently - All chunks linked together for seamless retrieval EXAMPLES: User: "Save progress" (working on Purmemo timeline feature) → System auto-generates: "Purmemo - Timeline View - Implementation" → Updates existing memory if this title was used before User: "Save this conversation" (discussing React hooks implementation) → Syste…