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plugin and server-side toolkit for hosting Mastra agents in Databricks Apps","maintainers":[{"name":"regbo","email":"reggie.pierce@gmail.com"},{"name":"reggie-db","email":"reggie.pierce@databricks.com"}],"readme":"# @dbx-tools/appkit-mastra\n\nAppKit plugin and server-side toolkit for hosting Mastra agents inside a\nDatabricks App.\n\nImport this package when an AppKit backend needs an agent service with\nDatabricks on-behalf-of auth, optional Lakebase-backed memory, Databricks Genie\ntools, model selection, chart/data embeds, MLflow feedback, and MCP exposure.\nThe package mounts the standard Mastra agent stream under the AppKit server, so\nclients can use Mastra-compatible chat transports instead of a custom protocol.\n\nKey features:\n\n- AppKit plugin lifecycle integration: routes, setup, shutdown, sibling plugin\n  access, and AppKit request context are handled inside `plugin.mastra()`.\n- Agent composition: define one or more Mastra agents, give each one local tools,\n  AppKit plugin toolkits, workspace skills, model defaults, and approval-gated\n  tools.\n- Databricks execution model: tool calls run with the active AppKit OBO client\n  where available, while storage and background work use service-principal\n  connections. Set `genieIdentity: \"service-principal\"` to run the agents'\n  Databricks calls as the app service principal instead, so callers who can open\n  the app but are not workspace members can still chat, or `\"auto\"` to make that\n  fallback per-request - OBO when the caller forwards a token, service principal\n  when they cannot.\n- Durable conversations: Lakebase-backed Mastra storage provides thread\n  history, message persistence, and optional vector memory.\n- Isolated command execution: auto-created workspaces use Databricks Sandbox by\n  default, created lazily per attributed user outside the App container.\n- Rich data answers: Genie tools, statement fetches, chart preparation, and\n  embed markers let an agent answer with text plus delayed chart/table payloads.\n- Operational surfaces: model-list routes, feedback routes, MCP exposure,\n  scoped API gating, tracing, and MLflow feedback are bundled with the plugin.\n\n## Why Not Just AppKit Agents?\n\nNative AppKit includes a beta Agents plugin with markdown and TypeScript agent\ndefinitions, AppKit tool-provider integration, streaming chat, thread\nmanagement, cancellation, and HITL approval. Use it when you want the AppKit\nagent model and do not need a separate agent framework.\n\nUse this package when you specifically want Mastra inside AppKit:\n\n- Mastra's larger plugin/tool ecosystem, MCP support, memory/storage model,\n  workflow primitives, and `@mastra/client-js` stream shape.\n- AppKit toolkits as Mastra tools, so Analytics, Files, Genie, and other AppKit\n  ToolProvider plugins stay available without rewriting them.\n- Genie as an agent tool that emits typed progress events, result metadata, and\n  delayed chart/data markers into the same assistant turn.\n- A paired React client in [`@dbx-tools/ui-mastra`](../../ui/mastra) with model\n  picking, thread sidebar, approvals, feedback, exports, and inline embeds.\n- Per-request model override and fuzzy endpoint resolution through\n  [`@dbx-tools/model`](../model), instead of binding every agent to a fixed\n  endpoint name.\n\n## Quick Start\n\n```ts\nimport { analytics, createApp, lakebase, server } from \"@databricks/appkit\";\nimport { agents, genie, plugin } from \"@dbx-tools/appkit-mastra\";\nimport { z } from \"zod\";\n\nconst analyst = agents.createAgent({\n  name: \"analyst\",\n  instructions: [\"You answer questions about workspace data.\", genie.GENIE_INSTRUCTIONS].join(\n    \"\\n\\n\",\n  ),\n  tools(plugins) {\n    return {\n      ...plugins.analytics.toolkit(),\n      ...plugins.genie?.toolkit(),\n      get_weather: agents.tool({\n        description: \"Get a simple weather report.\",\n        schema: z.object({ city: z.string() }),\n        execute: async ({ city }) => `Sunny in ${city}`,\n      }),\n    };\n  },\n});\n\nawait createApp({\n  plugins: [\n    server(),\n    analytics(),\n    lakebase(),\n    plugin.mastra({\n      agents: { analyst },\n      defaultAgent: \"analyst\",\n      genieSpaces: { sales: \"01ef...\" },\n    }),\n  ],\n});\n```\n\nBenefits of importing the package:\n\n- `plugin.mastra()` registers a full AppKit plugin named `mastra`.\n- `agents.createAgent()` keeps agent definitions typed and applies the default\n  Databricks workspace/skill mounts and Databricks Sandbox command execution.\n- `agents.tool()` lets the same AppKit-shaped tool body work in this Mastra\n  plugin.\n- `genie.GENIE_INSTRUCTIONS` and `plugins.genie.toolkit()` give agents a\n  Databricks Genie workflow without embedding a second agent.\n- Lakebase registration automatically enables durable thread storage and vector\n  memory unless you opt out.\n\n## Agent Registration\n\n`plugin.mastra({ agents })` accepts a single definition, an array, or a record.\nRecords are best when clients need stable agent ids:\n\n```ts\nplugin.mastra({\n  agents: {\n    support: agents.createAgent({ instructions: \"Answer support questions.\" }),\n    analyst: agents.createAgent({ instructions: \"Analyze workspace data.\" }),\n  },\n  defaultAgent: \"support\",\n});\n```\n\nWhen no agents are supplied, the plugin registers a built-in `default` analyst so\nthe route surface still works for smoke tests. Each agent is streamed through the\nMastra agent API mounted below the plugin path, typically `/api/mastra`.\n\nUse `agents.createTool` when you need Mastra-native fields such as\n`outputSchema`, `suspendSchema`, `requireApproval`, or MCP metadata. Use\n`agents.tool` for the smaller AppKit-compatible shape:\n\n```ts\nconst approveRefund = agents.createTool({\n  id: \"approve_refund\",\n  description: \"Approve a refund request.\",\n  inputSchema: z.object({ orderId: z.string(), amount: z.number() }),\n  requireApproval: true,\n  execute: async ({ context }) => approve(context.orderId, context.amount),\n});\n```\n\n## AppKit Toolkits\n\nThe `tools(plugins)` callback receives a dynamic index of registered AppKit\ntool-provider plugins. Each entry exposes `.toolkit(opts)` with AppKit-compatible\n`prefix`, `only`, `except`, and `rename` options.\n\n```ts\nconst agent = agents.createAgent({\n  instructions: \"Use the narrowest tool that answers the question.\",\n  tools(plugins) {\n    return {\n      ...plugins.analytics.toolkit({ only: [\"query\"] }),\n      ...plugins.files?.toolkit({ prefix: \"files.\", except: [\"delete\"] }),\n    };\n  },\n});\n```\n\nTool calls dispatch back through the owning AppKit plugin, preserving OBO auth\nand AppKit telemetry behavior. Optional plugins should be guarded with `?.` when\nyou spread their tools.\n\n### Tools Flow In, Not Out\n\nThe plugin is a tool _consumer_, not an AppKit `ToolProvider`: it deliberately\nimplements neither `getAgentTools()` nor `executeAgentTool()`, so its built-in\ntools (`ask_genie`, `get_space_description`, `get_space_serialized`,\n`get_statement`, `prepare_chart`, `render_data`, `summarize`) are reachable only\nfrom a Mastra agent turn this plugin serves.\n\nThat is a property of the tools, not a gap. Each one reads the per-request\nMastra execution context - the AppKit user stamped on `RequestContext`, the\n`writer` that streams Genie progress events to the chat, the per-call\n`abortSignal` - and refuses to run without it. An AppKit `ToolProvider` call\ncarries none of that, so exposing these through one would advertise tools that\ncannot work. Reach for\n[native AppKit Agents](https://developers.databricks.com/docs/appkit/v0) when you\nwant your agent tools callable by other AppKit hosts.\n\nNothing here can be auto-inherited by another host as a side effect: with no\nAppKit `ToolRegistry`, there is no `autoInheritable` surface to opt in or out\nof. Every built-in tool is also read-only (Genie questions, statement reads,\nchart planning, summarization), and the ambient tools stay off the MCP server\nunless `mcp: { tools: true }` names them explicitly. Approval-gated tools you\nregister yourself are enforced separately: boot fails if one is registered\nwithout Mastra storage to persist the suspended run.\n\n## Memory And Storage\n\nThe `memory` and `storage` config fields can be `false`, `true`, or a concrete\nMastra Postgres/PgVector config.\n\n```ts\nplugin.mastra({\n  agents: analyst,\n  storage: true,\n  memory: { id: \"analytics_memory\", tableName: \"agent_memory\" },\n});\n```\n\nWith `lakebase()` registered, both default to enabled:\n\n- storage uses a per-agent schema for durable threads and messages;\n- memory uses a shared vector index for semantic recall;\n- the service-principal pool is created outside any request so OBO user\n  identities are not captured in background storage work.\n\nWithout `lakebase()`, agents are stateless unless you provide explicit storage\nand memory configs.\n\n## Workspace Skills\n\nEvery `agents.createAgent()` gets a default Mastra `Workspace` from\n`workspaces.createWorkspace()`. It mounts Databricks Workspace files through the\ncurrent OBO user's `WorkspaceClient`, so Mastra can discover Assistant-style\n`SKILL.md` files at request time.\n\nLocations are a named map (`skillFolders`), so an app refers to a tree by name\nrather than repeating a path. Each name mounts at `/<name>` in the workspace\nnamespace (override with `mount`) and carries its own policy: `readable`\n(scanned for `SKILL.md`, default `true`) and `writable` (default `false`).\n`DEFAULT_SKILL_FOLDERS` supplies these:\n\n| Name                 | Location                           | Readable | Writable |\n| -------------------- | ---------------------------------- | -------- | -------- |\n| `workspace-team`     | `/Workspace/.assistant/skills`     | yes      | no       |\n| `workspace-team-app` | `/Users/<email>/.assistant/skills` | yes      | yes      |\n\nA consuming library merges over that map: a matching name overrides the\ndefault, `false` disables it, and any other name adds a location. A folder\npoints at a Databricks `path` (a literal, or a function resolving one per\nrequest) or supplies a ready `filesystem` for anything the OBO client cannot\nreach.\n\n```ts\nconst agent = agents.createAgent({\n  instructions: \"Use mounted workspace skills when relevant.\",\n  workspace: workspaces.createWorkspace({\n    skillFolders: {\n      // Point an existing name somewhere else.\n      \"workspace-team\": { path: \"/Workspace/Shared/team-skills\" },\n      // Drop a default entirely.\n      \"workspace-team-app\": false,\n      // Add an app-owned tree the agent may write back to.\n      runbooks: { path: \"/Workspace/Shared/runbooks/skills\", writable: true },\n      // Mount for file tools without adding it to skill discovery.\n      templates: { path: \"/Workspace/Shared/templates\", readable: false },\n      // Any Mastra filesystem works, including per-request ones.\n      volume: { filesystem: ({ requestContext }) => volumeFor(requestContext) },\n    },\n    mounts: [\n      async () => ({\n        mounts: { \"/reference\": myFilesystem },\n        skillPaths: [\"/reference/skills\"],\n      }),\n    ],\n  }),\n});\n```\n\nA folder whose location resolves to `undefined` is skipped for that request,\nwhich is how `workspace-team-app` drops out when no user email is stamped.\n`assistantSkills: false` starts from an empty map, leaving only the\n`skillFolders` given. Production workspace mounts require a forwarded token\nwith `workspace`, `workspace.workspace`, or `all-apis` scope. Development mode\nskips that gate for local iteration.\n\n## Databricks Sandbox\n\nAuto-created agent workspaces prefer the Beta\n[Databricks Sandbox](https://docs.databricks.com/aws/en/compute/serverless/sandbox)\nservice by default. The provider derives a stable opaque sandbox id from the\nworkspace and attributed user, creates the sandbox lazily on the first command,\nstarts a stopped sandbox, and waits for runnable state. Commands execute through\nthe synchronous Sandbox API and return Mastra's normal stdout, stderr, exit,\ntimeout, and truncation fields. When the Beta is definitively unavailable\n(404 or an explicit feature-disabled/preview-unavailable error), it falls back\nto the Node `@pydantic/monty` runtime so deployment and Python code execution\nstill work. Permission, authentication, and transient network failures remain\nvisible instead of silently changing providers.\n\nEnable Databricks Sandbox in the workspace Previews page before using command\ntools. The default adapter creates its own AppKit client through the normal\nenvironment/profile chain, so a Databricks App uses its service principal even\nwhen the agent turn uses OBO. Databricks Apps user authorization does not\ncurrently expose the Sandbox API scope. Outside Apps, a caller that supplies an\nexplicit OBO client must request the Sandbox API's `sandbox` scope. The Sandbox\nfilesystem is separate from Databricks Workspace skill mounts; command code must\ncopy data explicitly when it needs both.\n\nMonty is intentionally narrower than Databricks Sandbox: it accepts Python\nsource directly (or `python3 -c`), runs in crash-isolated subprocess workers,\nand exposes no host shell, filesystem, network, environment variables, or\nthird-party packages. Per-command cancellation kills the isolated worker, and\n`maxRetainedBytes` bounds retained stdout/stderr without dropping callback\nchunks. Prefer Python source for commands that must work on both providers.\n\nThe default needs no configuration:\n\n```ts\nplugin.mastra({\n  agents: analyst,\n});\n```\n\nTune the Databricks lifecycle globally for auto-created workspaces:\n\n```ts\nplugin.mastra({\n  agents: analyst,\n  sandbox: {\n    inactivityTimeout: \"1800s\",\n    startupTimeoutMs: 180_000,\n    commandTimeoutMs: 60_000,\n    fallback: \"monty\",\n  },\n});\n```\n\nDisable command execution, or explicitly replace Databricks with any Mastra\nsandbox on one agent:\n\n```ts\nplugin.mastra({ agents: analyst, sandbox: false });\nplugin.mastra({ agents: analyst, sandbox: \"monty\" });\n\nconst localAgent = agents.createAgent({\n  instructions: \"Run only trusted local commands.\",\n  workspace: workspaces.createWorkspace({\n    sandbox: myMastraSandbox,\n  }),\n});\n```\n\nAn explicit agent `workspace` always wins over plugin-level `sandbox` config, so\nproviders never run in parallel accidentally. `workspaces.createWorkspace()`\nalso accepts a custom resolver for per-request provider selection. A per-agent\nworkspace resolver returning `undefined` explicitly disables the workspace for\nthat agent.\n\n## Remote Skills\n\nWorkspace skills above are discovered from files ALREADY in the Databricks\nworkspace. `remoteSkills` provisions skills from OUTSIDE it at startup - a\nGitHub `owner/repo`, a git / GitLab URL, or a direct `SKILL.md` / archive\ndownload URL - so an app can ship with a curated skill set without anyone\nhand-uploading `SKILL.md` trees first.\n\n```ts\nmastra({\n  agents: { assistant: agents.createAgent({ instructions: \"...\" }) },\n  remoteSkills: [\n    \"owner/skill-repo\",\n    { source: \"https://example.com/skills/writing.md\", failOnError: false },\n  ],\n});\n```\n\nEach source is materialized at boot into an Assistant-style `SKILL.md` tree that\nthe default workspace then scans, so a provisioned skill behaves exactly like\none that was in the workspace all along. Resolution per source:\n\n- if the optional `skills` peer dependency is installed, the source is copied\n  into a staging dir with the `skills` CLI, which understands every source\n  format the ecosystem does (GitHub shorthand, git URLs, archive URLs);\n- otherwise the source URL is fetched directly and its `SKILL.md` written to the\n  staging dir. A non-URL source (e.g. bare `owner/repo`) without the `skills`\n  package installed cannot be resolved this way and fails.\n\nThe default destination is the Databricks workspace Assistant skills tree\n(`/Workspace/.assistant/skills`, the same tree a \"save this as a skill\" action\nwrites to), so provisioned skills persist across restarts and are picked up by\nthe built-in Assistant-skills mount. Pass `userEmail` to target that user's\n`/Users/<email>/.assistant/skills` instead, or `databricksBasePath` for an\nexplicit tree. When no Databricks client is resolvable at startup, the tree is\nwritten to a local temp dir and handed to Mastra as an extra local skill path\nfor the current process only.\n\nThe destination is **probed with a real write** before it is used, and falls back\nto that local tree when the probe fails. Writing the shared\n`/Workspace/.assistant/skills` tree is a workspace-ADMIN action, and a Databricks\nApp runs as a non-admin service principal, so the common deployment cannot write\nit - and the workspace API reports a path the caller may not touch as\n`RESOURCE_DOES_NOT_EXIST` rather than a permission error. Only the storage\nLOCATION degrades: the same skills are provisioned either way, so a\nnon-admin app keeps a working agent instead of failing startup. Give the app's\nservice principal `CAN_MANAGE` on the tree (or point `databricksBasePath` at one\nit owns) to get workspace persistence back.\n\nA source that resolves through neither path fails app startup, so a misconfigured\nskill set is caught at boot rather than silently missing. Set `failOnError: false`\n(top-level or per-source) to log and skip a bad source instead. Install the\noptional peer to enable the richest source formats:\n\n```sh\npnpm add skills\n```\n\n### Refresh policy\n\nProvisioning runs on every app boot, and skill trees change rarely, so each\nprovisioned tree carries a `.metadata.json` at its root recording when each\nsource was last downloaded. A source is only re-downloaded once that record is\nolder than a day; inside the window the existing tree is reused and no network\ncall is made. The record travels with the tree rather than living in process\nmemory, so a container that restarts a dozen times an hour pulls each source\nonce, not a dozen times.\n\n```ts\nmastra({\n  // Re-pull at most once an hour instead of once a day.\n  remoteSkills: { sources: [\"aitools\"], refreshTtlMs: 60 * 60 * 1000 },\n});\n\n// Per-source, and `0` to download on every boot:\nmastra({\n  remoteSkills: {\n    sources: [{ source: \"owner/skill-repo\", refreshTtlMs: 0 }, \"aitools\"],\n  },\n});\n```\n\nThe record is keyed by the source AND the options that change what it contains\n(`skills`, `experimental`, `ref`), so narrowing a skill list or moving a `ref`\nre-downloads immediately rather than serving the previous selection for a day.\nA missing or unreadable record is treated as a cache miss, never as a startup\nfailure.\n\n## Databricks AI Tools\n\n[Databricks AI Tools](https://github.com/databricks/databricks-agent-skills) are\nDatabricks-owned Agent-Skill trees (bundles, jobs, SQL, Genie, and more). The\n`\"aitools\"` source folds them into every default-workspace agent, so an agent\ngets first-class Databricks skills without anyone hand-copying `SKILL.md` files:\n\n```ts\nmastra({\n  agents: { assistant: agents.createAgent({ instructions: \"...\" }) },\n  remoteSkills: \"aitools\",\n});\n\n// A curated subset, plus another source alongside it:\nmastra({\n  remoteSkills: [\n    { source: \"aitools\", skills: [\"databricks-core\", \"databricks-jobs\"] },\n    \"owner/repo\",\n  ],\n});\n```\n\n**No CLI required.** `databricks aitools install` resolves its skills from the\nPUBLIC `databricks/databricks-agent-skills` repo, so this reads the same repo\ndirectly: it fetches the repo's generated `manifest.json` (which names each\nskill's files and whether it lives under `skills/` or `experimental/`) and\ndownloads them. That matters because a deployed Databricks App container has no\n`databricks` CLI - the old CLI shell-out simply never produced skills there.\nNo Databricks auth is involved either, since the repo is public.\n\nPer-source options for `\"aitools\"`:\n\n- `skills` - install only the named skills instead of the full stable set.\n- `experimental` - include the repo's `experimental/` skills (off by default).\n- `ref` - pin a tag / branch / sha. Defaults to `main`.\n\nBecause these skills track a public repo rather than the workspace, they are\nadded as LOCAL scan paths for the current process rather than uploaded to the\nDatabricks Assistant tree.\n\n## Genie Tools\n\n`genie.buildGenieTools()` and `plugins.genie.toolkit()` expose tools for:\n\n- asking a configured Genie space;\n- reading space descriptions and serialized space metadata;\n- fetching statement rows by `statement_id`;\n- preparing charts from Genie result sets.\n\nThe central agent drives those tools directly. Genie events stream through the\nMastra writer using the shared contract from\n[`@dbx-tools/shared-mastra`](../../shared/mastra), so clients can show thinking,\nSQL, row counts, summaries, chart markers, and data markers as the turn runs.\nIndependent `ask_genie` calls can run in parallel. One invocation owns the\nthread's reusable Genie conversation while overlapping invocations use isolated\nconversations, preventing concurrent messages from colliding in one Genie\nconversation. Sequential calls continue to reuse conversation context.\n\n`ask_genie` uses the Genie Agent Mode SSE API by default, projecting reasoning,\nSQL function calls, query output, and the synthesized answer into the existing\nwriter events and terminal `GenieMessage`. Set `genieAgentMode: false` on\n`mastra(...)` to force the legacy Conversation API polling flow. A pre-stream\n`FEATURE_DISABLED` or preview-toggle error falls back to polling automatically.\nAgent Mode carries query results inline as Markdown; use polling when a workflow\nspecifically requires legacy `statement_id`-backed chart or data embeds. For\nAgent Mode charts, pass the inline Markdown or structured metadata rows to the\nauto-wired `render_data` tool; never pass response, conversation, or function\ncall ids to `prepare_chart`.\n\n```ts\nconst agent = agents.createAgent({\n  instructions: `${baseInstructions}\\n\\n${genie.GENIE_INSTRUCTIONS}`,\n  tools(plugins) {\n    return { ...plugins.genie?.toolkit({ prefix: \"\" }) };\n  },\n});\n```\n\n## Charts And Data Embeds\n\n`chart.prepareChart()` mints a chart id immediately, caches an in-progress\nrecord, resolves the data in the background, and stores a terminal chart or\nerror. `chart.fetchChart()` long-polls that cache for route handlers and custom\nclients.\n\nBoth take a `userKey`: the chart cache is namespaced by the caller's identity,\nso a chart id lifted from another user's transcript resolves to nothing and the\nembed route answers `404`. Use `config.resolveUserKey()`, which reads the AppKit\nuser off the Mastra request context and falls back to the ambient execution\ncontext. Outside a Mastra turn, where there is a request but no request context,\nuse `config.attributedUserId(getExecutionContext(), requestUserId(req))` instead.\nBoth spell the same rule, which matters under `genieIdentity:\n\"service-principal\"`: the shared credential is the app service principal, but\ncharts stay keyed to the forwarded caller, so a reader that keyed off the\ncredential alone would miss every chart and show it as expired.\n\n```ts\nconst userKey = config.resolveUserKey(requestContext);\n\nconst { chartId } = await chart.prepareChart({\n  config: pluginConfig,\n  userKey,\n  title: \"Revenue by region\",\n  description: \"Compare total revenue by region.\",\n  resolveData: async () => ({ rows }),\n});\n\nconst resolved = await chart.fetchChart(chartId, { userKey });\n```\n\nAgents can return `[chart:<id>]` and `[data:<statement_id>]` markers in prose.\nThe embed route resolves them later, which avoids forcing the language model to\ninline large tables or wait for chart planning before continuing its answer.\nGenie supplies those statement ids through the legacy polling transport; Agent\nMode returns inline Markdown and structured table metadata instead. The\n`GENIE_INSTRUCTIONS` prompt directs Agent Mode turns to call `render_data` with\nthose inline rows, while polling turns call `prepare_chart` only with a real\nstatement id.\n\n### Chart Types And Hand-Written Charts\n\nThe planner does not emit a raw Echarts option. It fills a small plan (chart\ntype, categories, series) that `planToEchartsOption` expands, so tooltip,\nlegend, grid, and brand defaults stay consistent and a fast model has few ways\nto go wrong. The vocabulary is `bar`, `horizontalBar`, `line`, `area`, `combo`,\n`waterfall`, `scatter`, `heatmap`, `radar`, `pie`, `funnel`, `treemap`.\n\nSome of those are Echarts series types and some are not. `heatmap`, `radar`,\n`pie`, `funnel`, `treemap`, and `scatter` map to native series. `area` compiles\nto a line with an `areaStyle`, `horizontalBar` to a bar with swapped axes,\n`combo` to per-series types, and `waterfall` to the stacked transparent-helper\nbars Echarts documents, since it has no waterfall series.\n\nFor a chart the plan cannot express at all - sankey, boxplot, candlestick,\nsunburst, gauge, network graph, calendar, parallel coordinates, a `custom`\nseries - the planner picks `custom` and hand-writes the entire Echarts option\ninto the plan's `option` field. That option is passed through as-is: no axes,\ntooltip, or legend are grafted on, and only a centered title is filled in when\nthe object omits one. A configured `brand` theme still applies underneath, so\nanything the option sets wins over it.\n\n`option` is a JSON object encoded in a **string**, not a nested object, and\nthat is deliberate. The plan is the planner's provider-enforced structured\noutput, so a free-form object would become an unconstrained\n`additionalProperties` schema, which strict OpenAI and Gemini serving endpoints\nreject outright. That would break every chart rather than one. A string is\nuniversally representable, and a malformed one fails only its own chart. Values\nmust be plain JSON: a string-valued Echarts formatter is treated as a template,\nand nothing in the option is ever evaluated as code.\n\nPrefer a listed type whenever one fits. `custom` trades the shared defaults for\nreach, so it is the answer for an unsupported chart shape, not a way to restyle\na supported one.\n\n### Brand The Charts\n\nPass a `brand` to the plugin to theme every generated chart with your brand's\npalette and font; omit it for the default Echarts look.\n\n```ts\nimport { brand } from \"@dbx-tools/shared-core\";\n\nplugin.mastra({ agents, storage: true, brand: brand.defaultBrandContext });\n```\n\n`brand` is the portable `BrandContext` shared across the UI, email, and\nlibraries, so charts, email, and the chat UI theme from one source. The chart\nplanner derives an Echarts theme from it: a series color cycle seeded from\n`colors.primary` / `colors.accent` (plus a colorblind-friendly spread so\nmany-series charts stay legible) and the `typography.sans` font stack. Charts\nrender to canvas, so this is applied server-side on the Echarts option rather\nthan through the browser `[data-brand]` CSS bridge.\n\nWhat the planner does **not** set is any text color. A spec is planned here and\nread later in a browser whose light/dark theme the server cannot know, so the\nbrand's single (light) foreground would produce near-black labels on a dark chat\nsurface. The renderer resolves tick labels, axis names, grid lines, and the\ntooltip from AppKit's live CSS tokens instead - see\n[`@dbx-tools/ui-mastra`](../../ui/mastra)'s chart theming. Brand identity is the\nsame in either mode; chrome is not.\n\n## Model Selection\n\n`model.buildModel()` adapts the generic resolver from\n[`@dbx-tools/model`](../model) to Mastra. It resolves the model per request,\nso OBO identity and request-specific overrides stay isolated.\n\nModel priority is:\n\n1. request override (`X-Mastra-Model`, `?model=`, body `model` / `modelId`);\n2. per-agent `model`;\n3. plugin `defaultModel`;\n4. `DATABRICKS_SERVING_ENDPOINT_NAME`;\n5. workspace catalogue ranking and static fallback floor.\n\n```ts\nplugin.mastra({\n  agents: analyst,\n  defaultModel: \"claude sonnet\",\n  modelFuzzyMatch: true,\n  modelOverride: true,\n});\n```\n\nUse `serving.extractModelOverride()` and `serving.resolveServingConfig()` when\nbuilding custom routes that should behave like the plugin's `/models` and stream\nroutes.\n\nThe serving fetch interceptor repairs provider-specific wire requirements for\nboth `fetch(url, { body })` and `fetch(new Request(...))`. In particular,\nDatabricks-hosted GPT Astra Chat Completions with function tools receives\n`reasoning_effort: \"none\"` unless the caller already supplied a value. Claude\nreasoning replay and Gemini/Claude structured response content continue through\nthe same sanitizer.\n\nThe plugin also serves `GET /default-model` (and `/default-model/:agentId`),\nreturning `{ agentId, model, displayName }` - the static serving-endpoint an\nagent falls back to when the client pins no model, plus its humanized label.\n`model` / `displayName` are `null` when the agent resolves its model\ndynamically at call time. This lets a model picker label its default option\nwithout waiting on the `/models` catalogue (so it never flashes a raw id). A\n`:agentId` that is not registered returns `404` with the registered ids, the\nsame as the history and threads routes.\n\n## Threads, History, And Suggestions\n\nWhen storage is enabled, the plugin provides route helpers and in-process\nfunctions for conversation management:\n\n- `history.loadHistory()` and `history.clearHistory()` read or clear one thread;\n- `threads.listThreads()`, `threads.renameThread()`, and\n  `threads.deleteThread()` operate on the caller's scoped conversations;\n- `genie.collectSpaceSuggestions()` reads starter questions from the configured\n  Genie space.\n\nThe plugin resolves the active thread from `x-mastra-thread-id`, `?threadId=`,\nor a per-session fallback cookie. That keeps streaming, history, and clear\noperations aligned around the same conversation id.\n\n## Feedback And Observability\n\n`observability.buildObservability()` wires Mastra tracing when OTLP export is\nconfigured. `mlflow.resolveFeedbackEnabled()` turns MLflow feedback on when both\ntrace export and an MLflow experiment are configured, unless the plugin config\nforces a value. The plugin also stamps each chat turn's request/response onto\nthe HTTP root span via `traceIo.attachChatTurnTraceIo()` so MLflow's UC\n`*_trace_unified` view can show them (Mastra's own `mastra.agent_run.*`\nattributes sit on a child span that view never reads).\n\n```ts\nplugin.mastra({\n  agents: analyst,\n  feedback: true,\n});\n```\n\n`mlflow.logFeedback()` logs a human assessment against the active MLflow trace.\nThe response header name and request/response schemas live in\n[`@dbx-tools/shared-mastra`](../../shared/mastra).\n\n### Databricks Apps -> Unity Catalog (the supported path)\n\nManaged MLflow has **no** OTLP ingest endpoint. Do not set\n`OTEL_EXPORTER_OTLP_TRACES_ENDPOINT` to the workspace host - probes of\n`/api/2.0/mlflow/v1/traces`, `/otlp/v1/traces`, and similar paths 404. The\nmechanism that works is Databricks Apps telemetry: declare\n`telemetry_export_destinations` on the app resource so the platform injects a\nlocal OTLP sidecar (`OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4314`,\n`OTEL_EXPORTER_OTLP_PROTOCOL=grpc`) and persists spans to Unity Catalog. Point\nthe three tables at the MLflow experiment's existing UC trace location (do not\ninvent a parallel table set):\n\n```yaml\nvariables:\n  telemetry_schema:\n    default: my_catalog.my-traces-schema\n  mlflow_experiment_id:\n    default: \"123456789\"\n\nresources:\n  apps:\n    my_app:\n      # ...\n      config:\n        env:\n          - name: MLFLOW_EXPERIMENT_ID\n            value: ${var.mlflow_experiment_id}\n          # Apps ingress stamps traceparent on every request. Without this,\n          # every HTTP span is a child of a platform span that never lands in\n          # the UC tables, so `*_trace_unified` (root = empty parent_span_id)\n          # discards every chat turn.\n          - name: OTEL_PROPAGATORS\n            value: none\n      telemetry_export_destinations:\n        - unity_catalog:\n            traces_table: ${var.telemetry_schema}.${bundle.target}_otel_spans\n            logs_table: ${var.telemetry_schema}.${bundle.target}_otel_logs\n            metrics_table: ${var.telemetry_schema}.${bundle.target}_otel_metrics\n```\n\nAll three table fields are required. The app service principal needs\n`USE_CATALOG` / `USE_SCHEMA` / `SELECT` / `MODIFY` on that catalog.schema (the\nApps API also tries to grant access and fails with 403 if you lack `MANAGE` on\nthe catalog). Reject `mlflow-tracing` TypeScript SDK for this path: it claims\nthe global provider AppKit already owns and writes to a different store.\n\nSuccess in the boot log looks like:\n\n```\n[observability] Mastra observability wired through OTel bridge {\n  otelBase: 'http://localhost:4314', feedback: true, observability: 'mlflow'\n}\n```\n\nVerify with SQL against the UC `*_trace_unified` view (the REST\n`traces/search` API only covers the experiment store, not UC-backed traces).\nSchema names with hyphens need backticks; `attributes` is a `VARIANT`, so use\n`attributes:['key']::string` rather than `map_keys()`.\n\n## MCP Exposure\n\n`mcp.buildMcpServer()` exposes registered agents as MCP tools by default. The\nAppKit plugin publishes clean aliases under its base path:\n\n```ts\nplugin.mastra({\n  agents: analyst,\n  mcp: {\n    serverId: \"analytics\",\n    name: \"Analytics MCP\",\n    tools: false,\n  },\n});\n```\n\nUse `mcp: false` to disable MCP. Turn on `tools: true` only for ambient tools\nthat are safe outside an in-process chat turn.\n\n## Driving A Turn From Outside The Routes\n\nAnother plugin (or a scheduled job) can run an agent turn directly, but a raw\n`agent.generate(prompt)` loses everything the HTTP middleware stamps - most\nvisibly the AppKit user, which every user-scoped tool reads. `ask_genie` then\nfails with \"invoke the tool from an agent turn served by the mastra plugin\", so\nthe turn answers \"the data source is unreachable\" where the chat routes answer\nwith real data.\n\n`exports().createRequestContext()` builds the missing context:\n\n```ts\nconst mastra = context.getPlugins()?.get(\"mastra\")?.exports();\nconst requestContext = await mastra.createRequestContext({\n  threadId: conversationId,\n  resourceId: userId,\n});\nconst result = await mastra.getDefault().generate(prompt, { requestContext });\n```\n\nThe AppKit user, the memory thread / resource pair, and a request id (so the\nturn's spans join up in traces) are stamped exactly as the request middleware\nstamps them. Call it inside an `asUser(req)` scope to inherit the caller's OBO\nidentity; outside one it resolves to the service principal.\n[`@dbx-tools/teams`](../teams) uses this so a Teams card turn has the same tool\nreach - and therefore the same answer - as a chat turn.\n\n## API Gate\n\nThe stock `@mastra/express` app has broad management routes. The plugin's\ndefault `apiAccess: \"scoped\"` allows only the chat, read-only metadata,\nplugin-owned `/route/*`, embed, model, suggestion, and MCP surfaces that the\nclient needs. Use `apiAccess: \"full\"` only for a trusted first-party console.\n\n`server.isMastraRequestAllowed()` is exported for tests and custom dispatch\nlogic that need the same allowlist.\n\n## Routes\n\nMounted under the plugin base path, which is `/api/mastra` unless you override\n`name`. Every route below is registered through AppKit's `route()` helper, so it\nappears in the plugin's endpoint map and forwards handler errors to AppKit.\n\n| Method                     | Path                        | Purpose                                                                                     |\n| -------------------------- | --------------------------- | ------------------------------------------------------------------------------------------- |\n| `GET`                      | `/models`                   | Serving-endpoint catalogue for a model picker.                                              |\n| `GET`                      | `/default-model[/:agentId]` | Static default model an agent falls back to, with its humanized label. `404` on unknown id. |\n| `GET`                      | `/suggestions[/:agentId]`   | Starter questions from the configured Genie spaces. Degrades to `[]`.                       |\n| `GET`                      | `/embed/chart/:id`          | Long-polls a `[chart:<id>]` marker's cached spec. `?timeoutMs=` up to 5 minutes.            |\n| `GET`                      | `/embed/data/:id`           | Rows behind a `[data:<statement_id>]` marker. `?limit=` clamped server-side.                |\n| `GET` / `DELETE`           | `/route/history[/:agentId]` | Load or clear the caller's thread messages.                                                 |\n| `GET` / `DELETE` / `PATCH` | `/route/threads[/:agentId]` | List, delete, or rename the caller's conversations.                                         |\n| `POST`                     | `/route/feedback`           | Log a thumbs / comment assessment to the turn's MLflow trace. `404` when feedback is off.   |\n| `POST` / `GET`             | `/mcp`, `/sse`, `/messages` | MCP transports, when `mcp` is enabled.                                                      |\n\nAgent inference itself rides the stock Mastra routes (`/agents/:id/stream`), so\n`@mastra/client-js` and `@dbx-tools/ui-mastra` work without a bespoke protocol.\n\n## Environment Variables\n\nEvery value can also be set through plugin config, which wins. These are the\nfallbacks, so a deployment that already follows AppKit's Databricks env naming\nneeds no extra wiring.\n\n| Variable                                                            | Effect                                                                                                                  |\n| ------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |\n| `DATABRICKS_SERVING_ENDPOINT_NAME`                                  | Model used when neither the agent nor `defaultModel` names one.                                                         |\n| `DATABRICKS_GENIE_SPACE_ID`                                         | Genie space registered under the `default` alias.                                                                       |\n| `MASTRA_GENIE_IDENTITY`                                             | `user` (default, OBO), `service-principal`, or `auto` for the agents' Databricks calls.                                 |\n| `OTEL_EXPORTER_OTLP_ENDPOINT`, `OTEL_EXPORTER_OTLP_TRACES_ENDPOINT` | Presence of either turns Mastra tracing on when `observability` is unset. On Apps, the telemetry sidecar injects these. |\n| `OTEL_PROPAGATORS`                                                  | Set to `none` on Databricks Apps so ingress `traceparent` does not hide every chat trace from UC `*_trace_unified`.     |\n| `MLFLOW_EXPERIMENT_ID`, `MLFLOW_EXPERIMENT_NAME`                    | With an OTLP endpoint, turns MLflow feedback on when `feedback` is unset.                                               |\n\n## Configuration Reference\n\nThe plugin config is intentionally centered on the AppKit lifecycle instead of\nrequiring callers to assemble a Mastra server by hand.\n\n- `agents` registers a single agent, an array, or a record keyed by stable agent\n  ids. Records are best for UIs because the ids become route-visible.\n- `defaultAgent` controls which registered agent handles requests that do not\n  name an agent explicitly.\n- `storage` and `memory` accept `true`, `false`, or concrete Mastra Postgres /\n  PgVector options. `true` resolves from `lakebase()` when present.\n- `sandbox` defaults to Databricks Sandbox with Node Monty fallback for\n  auto-created workspaces. `false` disables command execution, `\"monty\"`\n  selects Monty directly, and `true` or an object selects/configures\n  Databricks.\n- `remoteSkills` provisions `SKILL.md` sources from outside the workspace at\n  startup (see [Remote Skills](#remote-skills)). Accepts a single source, a\n  list, or an options bag with `failOnError`, `userEmail`,\n  `databricksBasePath`, and `refreshTtlMs` (how long a provisioned tree is\n  reused before re-downloading, a day by default). A source is `\"aitools\"` (see\n  [Databricks AI Tools](#databricks-ai-tools)) or any URL-like.\n- `genieSpaces` maps aliases to Genie Space IDs (or to\n  `{ spaceId, hint }` objects). Those aliases flow into tool names,\n  suggestions, and chart/data workflows. An alias present with no space id is a\n  wiring contradiction and fails at construction rather than silently\n  registering no Genie tools.\n- `genieAgentMode` defaults to `true` for the streaming Agent Mode API. Set it\n  to `false` to force legacy polling. Pre-stream feature-disabled or\n  preview-toggle responses fall back automatically.\n- `defaultModel`, `modelOverride`, and `modelFuzzyMatch` control how loose model\n  names are resolved through Databricks Model Serving.\n- `feedback` controls whether MLflow feedback routes are exposed. The automatic\n  mode enables feedback when tracing and an MLflow experiment are configured.\n- `mcp` controls whether agents are exposed as MCP tools and how that server is\n  named.\n- `genieIdentity` picks the Databricks identity the agents' workspace calls run\n  as - the serving catalogue behind the model picker, Genie suggestions,\n  `ask_genie`, and the statement fetch behind a `[data:<id>]` embed. `\"user\"`\n  (default) is always on-behalf-of the signed-in user, so callers must be\n  members of the WORKSPACE, not just the Databricks account. `\"service-principal\"`\n  runs those calls as the app service principal instead, so any caller who can\n  open the app works even without workspace membership - useful when an app is\n  shared with an account-level group too large to add to the workspace. It\n  changes only the Databricks credential: memory threads, the per-user cache\n  namespace, and trace metadata still key off the forwarded user, at the cost\n  of per-user attribution in Genie / Unity Catalog. `\"auto\"` decides PER REQUEST:\n  OBO when the request actually carries an OBO token, service principal when it\n  does not. That is the mode for an app served through more than one door - a\n  container fronted by [`@dbx-tools/tunnel`](../../node/tunnel) serves both\n  email-code callers (no Databricks credential exists to forward) and the\n  platform front door on one port, and `\"user\"` would make every tunnel turn fail\n  with AppKit's `AuthenticationError` while `\"service-principal\"` would throw away\n  per-user scoping for the front-door callers who still have it. Falls back to\n  `MASTRA_GENIE_IDENTITY`. The decision itself lives in\n  [`@dbx-tools/appkit`](../appkit)'s `identity` module, so a non-Mastra plugin can\n  make the same call.\n- `apiAccess` chooses the route allowlist. Keep the default scoped mode for\n  deployed apps.\n\nUse this package when you want an AppKit-native agent runtime. Use the shared\nschemas in [`@dbx-tools/shared-mastra`](../../shared/mastra) when building a\nclient that talks to these routes.\n\n## Modules\n\n- `plugin` - `MastraPlugin` and `mastra()` AppKit plugin factory.\n- `agents` - `createAgent`, `tool`, `createTool`, agent build helpers, fallback\n  defaults, and approval-gated tool inspection.\n- `config` - plugin config types and RequestContext key constants.\n- `model` / `serving` / `servingSanitize` - Mastra model config, request\n  overrides, serving-endpoint config, and the on-the-wire request/response\n  cleanup that keeps provider-specific payload quirks (Astra tool reasoning,\n  Claude's replayed thinking blocks, Gemini/Claude content-parts responses)\n  from failing a turn.\n- `genie` - Genie prompt, space normalization, Genie toolkits, and suggestions.\n- `chart` / `statement` / `writer` - chart cache, statement row fetches, and\n  safe writer events.\n- `history` / `threads` / `pagination` / `validation` - conversation persistence\n  helpers, route handlers, and request-body validation.\n- `defaults` - cache / retry / timeout settings for the plugin's own outbound\n  calls, one constant per call site with its reasoning.\n- `style` - `TYPOGRAPHY_RULE`, the one no-emoji / no-em-dash sentence the agent\n  style block, the summarizer, and the thread titler all append, so a summary or\n  a thread title cannot drift from the prose it sits beside.\n- `memory` / `storageSchema` - Lakebase-backed Mastra store/vector setup.\n- `sandbox` - Databricks Sandbox lifecycle, fallback policy, and command-result\n  adapter.\n- `montySandbox` - Python-only Pydantic Monty fallback using Node subprocess\n  workers.\n- `workspaces` / `filesystems` - Mastra workspace creation with a default\n  Databricks sandbox plus named\n  `skillFolders` (defaults `workspace-team` / `workspace-team-app`, overridable\n  by consumers); `filesystems(fs)` wraps any `@dbx-tools/shared-fs`\n  `FileSystem` (including `@dbx-tools/databricks` / `@dbx-tools/fs`) as a\n  Mastra mount, with `scratchFilesystem` (fresh `tmpFS` + random id) when no\n  other mount resolves.\n- `remote-skills` - startup provisioning of remote `SKILL.md` sources into the\n  Databricks Assistant skills tree (or a local temp dir): the `\"aitools\"`\n  constant reads Databricks' own skill repo directly, and any other source goes\n  through the optional `skills` CLI or a direct fetch. Each tree carries a\n  `.metadata.json` so a source is re-downloaded at most once a day\n  (`refreshTtlMs`).\n- `mcp` - MCP server construction.\n- `observability` / `mlflow` / `traceIo` - tracing, feedback, and stamping chat\n  turn I/O onto the HTTP root span for MLflow's UC `*_trace_unified` view.\n- `server` / `rest` / `processors` - Express dispatch, Databricks REST helpers,\n  stream/result processors.\n\nBrowser-facing wire types are in\n[`@dbx-tools/shared-mastra`](../../shared/mastra). Genie event contracts are in\n[`@dbx-tools/shared-genie`](../../shared/genie). Model request/result contracts\nare in [`@dbx-tools/shared-model`](../../shared/model). The matching React chat\nsurface is [`@dbx-tools/ui-mastra`](../../ui/mastra).\n","readmeFilename":"README.md"}