Initialize AgentDB

Sets up a fresh AgentDB instance for the current project. Creates a single-file .rvf Cognitive Container that holds vectors, indexes, learning state, and the audit log.

When to use

• User asks to "set up agentdb", "init memory", "add a vector store"
• New project that needs persistent agent memory
• Existing project moving from in-memory state to durable memory

Steps

1. Confirm the storage path (default: ./memory.rvf at the project root, or ~/.agentdb/<project-name>.rvf for global memory).
2. Pick the embedder (default: Xenova/all-MiniLM-L6-v2 at 384d — fast, free, runs in-process).
3. Register the agentdb MCP server in Claude Code:

   claude mcp add agentdb -- npx agentdb@latest mcp start
   

4. Initialize the file via the MCP tool agentdb_pattern_store (the first store auto-creates the schema), or via CLI:

   npx agentdb@latest init ./memory.rvf
   

5. Add *.rvf to .gitignore unless the user explicitly wants memory checked into source control.
6. Confirm with a smoke test: store one pattern and search for it.

Notes

• The .rvf is a single binary file. Back it up like a SQLite database.
• For multi-agent setups, share one .rvf per coordinated namespace; use separate files for trust-boundary isolation.
• All AgentDB operations after init go through the MCP tools (agentdb_pattern_*, agentdb_reflexion_*, etc.) or the npm library (import { SelfLearningRvfBackend } from 'agentdb').

Don't

• Don't commit the .rvf file by default — it can hold session-specific data, including content from messages.
• Don't run init on an existing .rvf file without confirming — it will refuse rather than overwrite, but a confused user might delete the existing file thinking it's stale.