memory

# Memory Persistent memory across sessions using ChromaDB vector search. Every conversation is stored and retrievable. ## How It Works The memory system uses ChromaDB (local vector database, no server needed) to store conversation context as embeddings. When you start a new session, the agent searches past memory for relevant context. - **Storage**: `~/.shokunin/memory/chroma_db/` (ChromaDB persistent files) - **Sessions**: `~/.shokunin/memory/sessions/` (markdown summaries per session) - **MCP Server**: `~/.shokunin/memory/mcp-server.py` ## Workflow ### Step 1: Start memory server (if not running) The memory MCP server is configured in opencode.json. It starts automatically when OpenCode connects to it. ### Step 2: Save context during session At the end of each significant task, save context: ``` store_context with: text: "Summary of what was done, key decisions, code patterns" tags: ["project-name", "feature", "language"] project: "project-name" session_id: "current-session-id" ``` ### Step 3: Search past memory at session start When starting a new session, search for relevant context: ``` search_context with: query: "what we discussed about auth" project: "current-project" ``` ### Step 4: Get full session summary ``` get_session_summary with: session_id: "session-id" ``` ## Automatic Session Save The agent should automatically: 1. At the end of the session, save a summary of key decisions and context 2. At the start of a new session, search f...