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@dbx-tools/appkit-graphiti

v0.6.212

Published

AppKit process plugin for the Python dbx-tools Graphiti MCP runtime

Readme

@dbx-tools/appkit-graphiti

AppKit process plugin for the Python dbx-tools-graphiti MCP runtime.

Key features

  • starts Graphiti as a supervised AppKit sidecar;
  • installs the version-matched dbx-model-proxy release through the generated @dbx-tools/cli Rust command registry;
  • enables the Python Postgres write journal when the app supplies Lakebase or PostgreSQL connection environment;
  • starts an internal loopback Caddy proxy in front of upstream Graphiti, then republishes the allowed tools through AppKit at /api/graphiti/mcp;
  • derives a stable private Graphiti group from the AppKit user or Mastra memory resource id, overriding every caller-supplied group;
  • exposes only operations that can be constrained to that group and removes UUID arguments that could reference another user's graph objects;
  • selects separate free loopback ports for Graphiti, the model proxy, and Caddy unless they are explicitly configured;
  • exposes the user-scoped tools through plugins.graphiti?.toolkit() for @dbx-tools/appkit-mastra agents;
  • runs Graphiti and Caddy under concurrently, while the Python launcher runs Graphiti and dbx-model-proxy under Honcho;
  • propagates termination signals through both supervisors, which escalate unresponsive process groups to SIGKILL;
  • exposes the MCP path through plugin exports.

Why use this over native AppKit

AppKit has no Graphiti or embedded MCP sidecar. This package owns AppKit routes and process lifecycle while dbx-tools-graphiti owns Graphiti, Neo4j, the model proxy, and Postgres recovery.

Register

Install dbx-tools-graphiti in the app's Python dependencies and register the plugin:

import { server } from "@databricks/appkit";
import { appkit } from "@dbx-tools/appkit";
import { plugin as graphitiPlugin } from "@dbx-tools/appkit-graphiti";

await appkit.createApp({
  plugins: [server(), graphitiPlugin.graphiti()],
});

AppKit continues to listen on DATABRICKS_APP_PORT. The plugin adds a user-scoped MCP server at /api/graphiti/mcp; Graphiti, the model proxy, and Caddy remain loopback-only. The plugin's manifest declares the Lakebase resource requirements used by generated deployments, so Graphiti does not require a separate lakebase() plugin. Local callers must still supply a Lakebase or PostgreSQL connection environment to enable the journal.

Add the MCP tools to a Mastra agent with:

tools(plugins) {
  return { ...plugins.graphiti?.toolkit() };
}

The same resource id that scopes Mastra memory scopes every Graphiti operation invoked as an agent tool. Direct MCP requests use the forwarded AppKit user id, falling back to AppKit's service identity in local development. The plugin hashes that identity before using it as a Graphiti group id. Model or MCP input cannot choose group_id or group_ids.

The scoped surface includes memory ingestion, node/fact search, episode listing, saga summaries, community building, triplet insertion, and status. UUID-only read/delete operations and graph clearing are omitted from the agent and public MCP surfaces.

Configuration

Plugin config overrides environment values:

  • graphitiPort / GRAPHITI_PORT: Graphiti's internal port; a free loopback port is selected automatically when omitted;
  • modelProxyPort / MODEL_PROXY_PORT: the managed model-proxy port; a separate free loopback port is selected automatically so another local proxy cannot be mistaken for Graphiti's model backend;
  • proxyPort / PROXY_PORT: Caddy's internal port; a third free loopback port is selected automatically when omitted;
  • python / PYTHON: Python executable, default python3;
  • journalNamespace / JOURNAL_NAMESPACE: Postgres journal namespace, default DATABRICKS_APP_NAME, then the detected project name, then default.

The Python runtime defaults the journal table to dbx_tools_graphiti.graphiti_write_journal and provisions that schema when absent, so a Lakebase service principal does not need write access to public.

The app must package both Node and Python dependencies. Databricks Apps supports this directly with package.json plus pyproject.toml or requirements.txt.

Modules

  • config resolves sidecar ports, the Python command, and journal namespace;
  • plugin starts and supervises Graphiti and Caddy.