# Chariton Kyp Memgentic Memgentic > Memgentic — Universal AI Memory Layer - Skill: `tomevault-io/chariton-kyp-memgentic-memgentic` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/chariton-kyp-memgentic-memgentic` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/chariton-kyp-memgentic-memgentic/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/chariton-kyp-memgentic-memgentic --- # Memgentic — Universal AI Memory Layer Memgentic captures knowledge from all your AI tools and makes it searchable. Every memory carries full provenance: which tool, which session, when it happened. ## When to Use Search memory **before** doing work, not after: - **Starting a new task** — Check for prior decisions, context, or related work. - **Making architectural decisions** — Check if this was discussed before. - **Debugging** — Search for related past issues and solutions. - **User asks about history** — "what did we decide about...", "remember when...", "how did we handle..." - **Encountering unfamiliar code** — Check for context on why it was written that way. - **Setting up or configuring something** — Check for user preferences and conventions. When in doubt, search. It costs little and prevents repeated work. ## CLI Commands ### Semantic Search (primary) ```bash # Search memories by meaning memgentic search "database migration strategy" # Compact output — fewer tokens, good for scanning memgentic search "database migration strategy" --format compact # Filter by platform memgentic search "auth implementation" -s claude_code memgentic search "API design" -s chatgpt # Filter by content type memgentic search "why we chose PostgreSQL" -t decision memgentic search "user coding style" -t preference ``` ### Store a Memory ```bash # Save an important fact or decision memgentic remember "We chose Qdrant over Pinecone for local-first vector storage" # Memory type is auto-classified from content memgentic remember "Always use UV for package management" ``` ### Other Commands ```bash # See which platforms have memories and how many memgentic sources # Generate standalone context file with recent activity memgentic update-context # Explore knowledge graph around an entity memgentic graph "authentication" # Check system health memgentic doctor ``` ## MCP Tools If the Memgentic MCP server is running, these tools are available directly: | Tool | Purpose | |------|---------| | `memgentic_recall` | Semantic search with source filtering | | `memgentic_remember` | Store a new memory | | `memgentic_search` | Full-text keyword search | | `memgentic_recent` | Recent memories by timestamp | | `memgentic_briefing` | Cross-agent briefing of recent activity | | `memgentic_sources` | List platforms and memory counts | | `memgentic_configure_session` | Set session-level source filters | | `memgentic_stats` | Memory statistics and analytics | | `memgentic_forget` | Archive (soft-delete) a memory | | `memgentic_export` | Export memories as JSON | ### MCP Examples ``` memgentic_recall(query="authentication flow", limit=5) memgentic_recall(query="auth", sources=["claude_code", "chatgpt"]) memgentic_remember(content="Project uses JWT with refresh tokens", memory_type="decision") memgentic_search(query="PostgreSQL", content_type="decision") memgentic_configure_session(exclude_sources=["codex_cli"]) memgentic_briefing(hours=24) ``` ## Efficient Retrieval Strategy Minimize token usage with a progressive approach: 1. **Start compact** — Use `--format compact` for an overview of what exists. 2. **Narrow down** — Add platform filter (`-s claude_code`) or type filter (`-t decision`) when you know what you are looking for. 3. **Full detail** — Drop `--format compact` only when you need the complete memory content. 4. **Limit results** — Use `--limit` to cap the number of results returned. ```bash # Step 1: Quick scan memgentic search "deployment" --format compact --limit 10 # Step 2: Found relevant results, get details from a specific platform memgentic search "deployment" -s claude_code --limit 3 ``` ## Memory Types Memories are classified into types for filtering: | Type | Contains | |------|----------| | `decision` | Architectural and technical decisions with rationale | | `learning` | Things learned during development | | `preference` | User preferences, conventions, coding style | | `fact` | General knowledge and project context | | `bug_fix` | Bug fixes, root causes, and solutions | | `conversation_summary` | Summaries of complete sessions | Filter by type to get precise results: ```bash memgentic search "database" -t decision # Only decisions about databases memgentic search "testing" -t preference # Only testing preferences memgentic search "auth" -t bug_fix # Only auth-related bug fixes ``` ## Cross-Platform Context Memgentic captures from multiple AI tools. Each memory records its source platform: | Platform | Source ID | |----------|-----------| | Claude Code | `claude_code` | | Gemini CLI | `gemini_cli` | | ChatGPT | `chatgpt` | | Aider | `aider` | | Codex CLI | `codex_cli` | | Copilot CLI | `copilot_cli` | | Claude Web/Desktop | `claude_web` | | Antigravity | `antigravity` | Use source filtering to scope searches: ```bash # What did we discuss in Claude Code about this project? memgentic search "project architecture" -s claude_code # What did ChatGPT suggest about this topic? memgentic search "caching strategy" -s chatgpt # See all available sources memgentic sources ``` ## Capture Methods Memories enter the system through: - **auto_daemon** — File watcher captures conversations automatically in the background. - **mcp_tool** — Stored via MCP tool calls during AI sessions. - **cli** — Manually saved via `memgentic remember`. - **json_import** — Bulk imported from conversation exports (ChatGPT, Claude Web). ## Setup ```bash # Check prerequisites memgentic doctor # Interactive setup (model selection, configuration) memgentic setup # Import existing conversations from all detected AI tools memgentic import-existing # Start background capture daemon memgentic daemon # Start MCP server for AI tool integration memgentic serve ``` --- > Source: [Chariton-kyp/Memgentic](https://github.com/Chariton-kyp/Memgentic) — distributed by [TomeVault](https://tomevault.io).