Tortoise on your own infrastructure

What you get: a local FalkorDB graph (or embedded), the MCP server over stdio, and the same 5-question onboarding prompt your agent walks you through. The MCP tool names are identical to hosted — only the transport differs (stdio vs Streamable HTTP).

Python 3.11+ required. No Docker needed for the embedded mode.

pip install tortoise-graph   # or: pip install -e . from the repo

Run tortoise onboard from your repo — it chains init → index → demo → doctor automatically:

tortoise onboard

Step 1/5: Ensure Tortoise SDK is installed
  ✅ Tortoise installed
Step 2/5: Initialize graph
  ✅ graph ready
Step 3/5: Index repository
  Found 42 markdown files. Indexing…
Step 4/5: First memory demo
  ✅ demo points created
Step 5/5: Health check
  ✅ doctor OK

Onboarding complete.
Next: tortoise serve  — start MCP server for agents
      tortoise setup  — configure per-role memory

Idempotent — re-running skips already-done steps. When it finishes, it prints the canonical onboarding prompt URL — paste that into your agent to complete setup.

Connect your agent over stdio (local process) instead of Streamable HTTP. Pick your harness:

Configure how Tortoise filters memory per role:

tortoise setup --role developer   # researcher | strategist | developer

Or run tortoise setup interactively — it walks through episodic / epistemic / semantic / procedural / working memory per role.