databricks-mason

Mason is an experimental CLI for Databricks custom agent preview APIs and deployments. It manages memory, sessions, tracing, and deployments from one authenticated command.

The underlying APIs are in preview and may need workspace enablement.

Overview

A managed path from your custom agent code to a production-ready, scalable, durable agent hosted on Databricks in minutes - with no server framework to build, no infrastructure to provision, and no invocation protocol to design yourself. Bring your own agent, or start from a template.

• Deployment - a guided lifecycle (scaffold, run locally, deploy) that turns an agent project into a hosted endpoint. Databricks provisions the compute, the stores your agent binds (session, memory), and the access grants, so you ship application code and get a running endpoint.
• Runtime - a managed HTTP invocation contract (synchronous, streaming, background) plus optional durable execution (persistence, heartbeats, crash recovery) backed by Databricks Lakebase, with no database or job queue to operate. Use the opinionated AgentApp server to get it out of the box, or bring your own server for full control.

Deployment

• Agent project - mason init scaffolds a deployable project from a framework template (LangGraph or OpenAI Agents) with the runtime, tests, and an optional chat UI wired up; you edit the application code (model, tools, prompts).
• agent.toml - the declarative source of truth for the Databricks-managed infrastructure your agent depends on: tool bindings (data sandbox, managed MCP services, Unity Catalog functions) and memory, session, and durability resources. mason deploy reads it to provision and wire everything up (detailed under Agent tools).
• mason deploy - provisions the bound stores, grants the app's service principal access to them, provisions the durable-runtime database when durability is on, configures tracing, and rolls out the app. mason deployments covers the lifecycle (list, get, logs, start, stop, delete).
• mason dev - runs your agent from the same manifest the deployment uses, so local behavior matches what ships.

Runtime

The two ways to run an agent:

• AgentApp - opinionated, batteries included. Register one handler and get Mason's full invocation contract (synchronous, streaming, background). Enable the durable runtime so long-running and background work survives restarts, redeploys, and crashes. The framework templates are thin layers over AgentApp (HTTP contract detailed under Runtime).
• Custom server - generic, full control. mason init --server custom scaffolds a minimal FastAPI server with no AgentApp: you define your own endpoints, request/response shapes, and protocol. mason dev and mason deploy run and ship it the same way.

Prerequisites

• Python ≥3.10 — the mason CLI installs and runs on any Python 3.10+. The memory, sessions, tracing, and tools commands need nothing else.
• uv — needed to scaffold, run, and deploy an agent (mason init → mason dev → mason deploy): the scaffolded project builds its environment and launches with uv run, both locally and in the deployed Apps runtime. Not needed for the store/session/tracing/tools commands above.
• Databricks CLI — needed for browser-based mason login. If a profile is already authenticated, Mason uses it directly and the Databricks CLI is optional.

Installation

From PyPI:

pip install databricks-mason

From source:

pip install 'git+https://github.com/databricks/databricks-ai-bridge.git#subdirectory=integrations/mason'

The base package includes the CLI, store SDK, and AgentApp HTTP runtime. Generated projects declare their framework dependencies automatically.

Shell completion

Add this to ~/.zshrc:

eval "$(_MASON_COMPLETE=zsh_source mason)"

Authentication

Mason uses Databricks authentication. Ask Mason to authenticate and remember a named profile:

mason login --profile <profile>
mason sessions stores list

mason login validates existing credentials first. If credentials are missing or rejected in an interactive terminal, Mason runs databricks auth login --profile <profile>, revalidates the profile, and stores the selection in ~/.mason/config.json. This browser-based setup requires the Databricks CLI. In non-interactive environments, authenticate the profile before running Mason. mason logout forgets the saved selection without revoking the underlying credentials.

If Databricks SDK default authentication is already configured, you can skip mason login. You can also pass the global --profile/-p option before an individual command, for example mason --profile <profile> tools list. Use --output json for scripting.

Quickstart

The shortest path from a blank directory to a running and deployed agent:

mason login --profile <profile>
mason init my-agent
cd my-agent
mason dev                 # run locally
mason deploy my-agent     # deploy to Databricks

mason dev runs the agent locally on http://localhost:8000, wrapping the Databricks Apps local runtime so local behavior matches a deployment.

mason deploy my-agent deploys a Databricks App named agent-mason-my-agent, provisions the stores declared in agent.toml, and grants the app's service principal access to them. mason deployments list shows what you have deployed, and mason deployments get my-agent prints its URL and status.

mason init declares default memory and session stores in agent.toml, so the deployed agent has long-term memory and durable conversation history. It creates <name>-<6-letter-token>-memory and <name>-<6-letter-token>-sessions, and records both names in agent.toml. mason deploy creates them if they don't exist yet. Point the agent at stores you already have with mason memory bind <name> / mason sessions bind <name>, or scaffold without stores using mason init --server custom (see Initialize the chat app demo).

To exercise the agent — locally under mason dev or once deployed — mason endpoint invoke sends it an HTTP request. MLflow tracing is on by default; mason tracing list shows the traces it produces.

Python SDK

MasonClient adds a small resource-oriented layer over the Mason API. Pass it an authenticated Databricks WorkspaceClient, or omit the argument to use the Databricks SDK's default authentication resolution:

from databricks.sdk import WorkspaceClient
from databricks_mason import MasonClient

mason = MasonClient(WorkspaceClient(profile="my-workspace"))

session_store = mason.session_stores.create("support-agent-sessions")
session = session_store.add(actor_id="customer-123", session_id="case-456")
session.append_items(
    [
        {"type": "message", "role": "user", "content": "I need help with my cluster."},
        {"type": "message", "role": "assistant", "content": "Let's take a look."},
    ]
)

memory_store = mason.memory_stores.create("coding-agent-memory")
memory = memory_store.add(
    actor_id="alice",
    path="/preferences/style.md",
    content="The user prefers concise answers.",
)
results = memory_store.search(
    actor_id="alice",
    query="response preferences",
    limit=10,
)
memory = memory.update(content="The user prefers very concise answers.")
memory.delete()

The root collections manage stores: mason.memory_stores.create/get/list and mason.session_stores.create/get/list. A returned store owns operations on its contents, such as memory_store.add(), memory_store.get("memory-id"), memory_store.list(), and memory_store.search(), or session_store.add(), session_store.get("session-id"), and session_store.list(). Returned memories, sessions, and stores own their update() and delete() operations.

All list() methods return iterators that automatically consume server pages. List page_size and search limit values must be between 1 and 100. session.list_items() also auto-pages. session.fork(...) creates an independent copy, optionally through a specific item. Deleting a session with descendants requires session.delete(force=True) to cascade the deletion.

The resource layer intentionally does not mirror every API method. Its private transport will be replaced by the generated WorkspaceClient.mason service when that is released, without changing this public surface. Deployment, sandbox, tracing, and the existing CLI commands remain separate.

Runtime

AgentApp runs your agent through one HTTP API for synchronous, streaming, and background invocations. Register an @app.invoke handler, publish progress with await context.emit(event), and return a JSON result. You can also add your own FastAPI endpoints.

Start from a template, edit the agent code in agent/, and run it locally before deploying:

mason init my-agent --framework langgraph --server mason --profile <profile>
cd my-agent
mason dev
# Stop the local server when ready to deploy.
mason --profile <profile> deploy my-agent

Use --framework openai for OpenAI Agents. Templates keep agent code separate from the runtime adapter and declare default Session and Memory Store bindings in agent.toml.

Each managed run is an invocation. Send a client-generated UUID id and your agent's input:

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "input": {"messages": [{"role": "user", "content": "Hello"}]},
  "background": true,
  "stream": true
}

Endpoint	Behavior
POST /api/invocations	Defaults to synchronous execution: 200 with the result under output. stream: true returns SSE events. background: true returns 202 with a status URL; adding stream: true also includes an events URL.
GET /api/invocations/{id}	Returns the invocation status and, when completed, its output.
GET /api/invocations/{id}/events?after={cursor}	Streams events after the last received event ID, allowing clients to reconnect.

The UUID also acts as an idempotency key: repeating the same request reuses the existing invocation while its record is retained; using the ID for a different request returns 409.

mason dev keeps execution state in process and loses it on restart. For projects with [agent].server = "mason", mason deploy provisions a persistent Runtime Store for requests, status, events, and results. Register @app.recover to restart interrupted work after worker failures. Recovery is at-least-once, so external side effects must be idempotent. Session and Memory Stores separately preserve the state used by your agent.

The managed path uses the internal Runtime Store API to create a dedicated database in the workspace's shared Lakebase project and give the app SP ownership. Mason initializes its schema and tables; no manual Lakebase grant or Postgres app-resource attachment is needed. Backend selection is an internal rollout detail, not a user-facing setting; Mason currently retains the legacy per-app Lakebase project by default. Once enabled, redeploy reads the stored backend and verifies the app identity, and mason deployments delete removes the managed store before deleting the app. The switch does not migrate existing deployments between backends. Managed cleanup errors retain the app for retry. Direct app deletion bypasses managed store cleanup.

Use server = "custom" to deploy your own HTTP server without provisioning a Runtime Store. Changing the server type of an existing deployment is not supported. To use a different server, scaffold a new project with the desired mason init --server option and deploy it under a new name. See the runtime guide for agent hooks, full API examples, and recovery behavior.

Memory and sessions

To hold context, an agent needs two kinds of state: the state of the interaction it is handling right now, and the durable knowledge it carries from one conversation to the next. Databricks provides a fully managed store for each, both backed by Lakebase and usable from agents built on any framework:

• Managed agent sessions store an agent's session state: the state an agent or framework keeps for one interaction. Most commonly this is the conversation history (the ordered transcript of messages, tool calls, and results), but it can be any state a framework persists, such as a LangGraph graph. The agent reads it at the start of a turn and appends to it as the interaction runs.
• Managed agent memory stores durable facts, preferences, and decisions that an agent recalls in later, separate conversations, retrieved by semantic search.

The examples below use the MasonClient Python SDK; the same operations are available as mason sessions / mason memory CLI commands.

Sessions

A session store holds sessions, and each session holds an ordered list of session items. A session is one interaction — typically a conversation thread — grouped under an actor_id (who it belongs to; set this from trusted application context, never a model- or user-supplied value) and identified by a caller-chosen session_id (the service generates one if you omit it). Each item is an opaque, JSON-compatible data value — a message, tool call, result, or reasoning block — that Databricks stores and returns verbatim, in order, and never mutates once appended.

Create a store, start a session, append the conversation's turns, and read the history back on a later request:

from databricks.sdk import WorkspaceClient
from databricks_mason import MasonClient

mason = MasonClient(WorkspaceClient())

session_store = mason.session_stores.create("support-agent-sessions")
session = session_store.add(actor_id="customer-123", session_id="case-456")

session.append_items(
    [
        {"type": "message", "role": "user", "content": "I need help with my cluster."},
        {"type": "message", "role": "assistant", "content": "Let's take a look."},
    ]
)

# On a later turn, reload the session and read its full history in order.
session = session_store.get("case-456")
history = [item.data for item in session.list_items()]  # list_items auto-pages

A session can be forked into an independent branch: a new session seeded with the original's history, linked back to its origin by parent_session_id. Fork the full history, or only up to a specific item, to explore an alternate continuation without disturbing the original thread:

branch = session.fork(actor_id="customer-123")  # add up_to_item_id=... to branch up to one item

Deleting a session that has such descendants requires session.delete(force=True) to cascade.

In a Mason-server agent you don't call these directly — the framework adapter reads and appends session state for you. With LangGraph, pass checkpointer() when you build the agent and scope each run with thread_config(session_id); the OpenAI Agents adapter exposes the same as session_store(session_id):

from databricks_mason.langgraph import checkpointer, thread_config

agent = create_agent(model=..., tools=[...], checkpointer=checkpointer())
result = await agent.ainvoke(inputs, config=thread_config(session_id))

Memory

A memory store holds memory entries. Each entry is a free-form content string plus a short description used for retrieval, keyed by three fields: actor_id (whose memory it is — set from trusted application context, never a model- or user-supplied value), path (a filesystem-like key within an actor, such as /preferences/response-style.md), and an optional session_id (the session an entry came from, for provenance). An entry is uniquely identified by its actor_id, path, and optional session_id.

Write an entry when the agent learns something durable, then recall it in a later, separate conversation with a natural-language search — results are ranked by full-text (BM25) relevance, up to 100 entries, with no pagination or vector similarity:

from databricks.sdk import WorkspaceClient
from databricks_mason import MasonClient

mason = MasonClient(WorkspaceClient())

memory_store = mason.memory_stores.create("support-agent-memory")
memory_store.add(
    actor_id="user-123",
    path="/preferences/communication.md",
    content="Prefers email over phone. Timezone: PST.",
    description="User 123 communication preferences",
)

# In a later, separate conversation, recall what the agent knows about this user.
results = memory_store.search(actor_id="user-123", query="communication preferences", limit=10)

To browse rather than search, memory_store.list(actor_id=..., path_prefix=...) returns entries directly.

In a Mason-server agent, add the memory tools so the model can read and write memory during a run. memory_tools(actor) exposes remember and recall bound to one actor's partition; it resolves the store from the [memory_store] binding — carried to the runtime by the AGENT_MEMORY_STORE env var that deploy and mason dev inject — and returns no tools when no store is bound, so the agent runs unchanged. The OpenAI Agents adapter exposes the same as memory_tools():

from databricks_mason.langgraph import memory_tools

agent = create_agent(model=..., tools=[*your_tools, *memory_tools(actor)])

actor_id partitions data; it is not access control. Both stores are workspace-scoped and authorized at the store level, so any principal that can reach a store can read and write every actor's entries. For strict isolation between tenants or users, use a separate store per boundary. Grant another principal — such as your app's service principal — access with session_store.grant_permission(principal_id) or memory_store.grant_permission(principal_id); mason deploy does this for the deployed app automatically.

Declaring and provisioning stores

For a deployed agent, agent.toml declares which stores it uses and mason deploy provisions them — you don't create stores by hand. mason init declares a default memory and session store named from the project; override those names, point at stores you already have, or let deploy create them:

# Scaffold a project with default memory and session stores declared in agent.toml.
mason init my-agent

# Override the declared store names at init time.
mason init my-agent --memory-store support-agent-memory --session-store support-agent-sessions

# Or point an existing project at specific stores (edits agent.toml only; creates nothing).
mason sessions bind support-agent-sessions
mason memory bind support-agent-memory

# deploy creates any declared-but-missing store and grants the app's service principal access.
mason deploy my-agent

Memory and session stores are independent resources: deleting one never affects the other.

Commands

For the full command reference - every command, subcommand, argument, and option, in table form - see cli.md. The tree below is a quick overview.

mason [-p <profile>] [-o text|json]
  login        [--profile P]
  logout
  init         [--framework openai|langgraph] [--server mason|custom]
               [--disable-chat-app]
               [--memory-store NAME] [--session-store NAME]
               [--profile P] [directory]
  dev          [--source PATH] [--prepare-environment] [--app-port PORT]
               [--with-traces C.S]
  memory
    bind         STORE [--source PATH]
    unbind       [--source PATH]
    stores     create | list | get | update | delete
    entries    create | get | list | search | update | delete
  sessions     create | list | get | update | delete | fork
    bind         STORE [--source PATH]
    unbind       [--source PATH]
    stores     create | list | get | update | delete
    items      list | append | pop | clear
  tracing
    configure  [--experiment E] [--source PATH]
    disable    [--source PATH]
    list | get
  tools
    add sandbox      --scope SCOPE [--scope SCOPE ...] [--source PATH]
    add mcp          SERVICE [--name NAME] [--source PATH]
    add uc-function  FUNCTION [--name NAME] [--source PATH]
    add genie-one    [--name NAME] [--source PATH]
    add genie-agent  SPACE_ID [--name NAME] [--source PATH]
    list             [--kind sandbox|mcp|uc-function|genie-one|genie-agent]
                     [--schema CATALOG.SCHEMA]
    remove           TOOL_ID [MCP_SERVICE] [--source PATH]
  deploy       <name> --source PATH [--with-traces C.S] [--instances N]
  deployments  list | get | logs | start | stop | delete
  endpoint
    invoke      [APP] --path PATH [--url URL] [--json JSON] [--sse]

Invoke HTTP endpoints

mason endpoint invoke is a low-level HTTP command. It resolves and authenticates a deployed Databricks App, or targets localhost and arbitrary servers through --url. It does not assume an agent protocol: provide the method, path, query parameters, and complete JSON body required by the server.

mason --profile <profile> endpoint invoke mason-my-agent \
  --path /api/invocations \
  --json '{"id":"00000000-0000-4000-8000-000000000001","input":[{"role":"user","content":"Hello"}]}'

mason endpoint invoke --url http://localhost:8000 \
  --path /api/invocations \
  --json '{"id":"00000000-0000-4000-8000-000000000001","input":[{"role":"user","content":"Hello"}]}'

The JSON body remains explicit even for Mason-generated agents. For example, Mason Runtime agents require a client-generated invocation ID, and streaming servers require their own streaming field plus --sse so the CLI consumes the response as Server-Sent Events.

INVOCATION_ID=$(uuidgen)
mason --profile <profile> endpoint invoke mason-my-agent \
  --path /api/invocations \
  --json "{\"id\":\"$INVOCATION_ID\",\"input\":[{\"role\":\"user\",\"content\":\"Run the report\"}]}"

mason --profile <profile> endpoint invoke mason-my-agent \
  --path /api/invocations \
  --sse \
  --json "{\"id\":\"$INVOCATION_ID\",\"input\":[{\"role\":\"user\",\"content\":\"Hello\"}],\"stream\":true}"

--session-id preserves one application session across calls by setting the Databricks Apps routing cookie. This also works with a direct App URL and with the generated runtime on localhost. OAuth and session headers are managed by Mason; arbitrary custom request headers are intentionally not exposed by this command.

Command help

Use the conventional help flag at any command level. Every command's help includes runnable examples:

mason --help
mason deploy --help
mason sessions items append --help

Agent tools

For projects with [agent].server = "mason" (the default from mason init), agent.toml is the declarative source of truth for Databricks-managed infrastructure: the Runtime Store, sandbox, managed MCP, Genie and Unity Catalog function bindings, plus memory and session resources. mason tools add updates only this file; direct TOML edits have the same behavior. Both Mason-server framework adapters read the managed bindings at runtime without generating or patching agent source:

mason tools add sandbox --scope table:samples.nyctaxi.trips
mason tools add mcp system.ai.web_search
mason tools add uc-function catalog.schema.lookup_ticket
mason tools add genie-one
mason tools add genie-agent SPACE_ID
mason tools remove mcp system.ai.web_search
mason tools list

For MCP services, the remove command accepts the same service name as the add command. You can also remove any binding by its id in agent.toml, for example mason tools remove web_search. Every successful add (including an already-configured no-op) points you to the target project's agent.toml to review configured managed tools and MCP bindings. With --source, the message points to that project's file. JSON add output includes its path in manifest.

mason tools list discovers available integrations to add, not configured bindings. By default it shows built-in add recipes and caller-visible MCP Services in system.ai. A recipe may still need your resources: sandbox scopes, a concrete UC function name, or a Genie Space ID. Genie One needs no additional argument. system.ai.sandbox is represented by its scoped recipe rather than a second unscoped add command. The list does not enumerate every workspace schema, individual operations inside MCP services, or custom Python tools.

mason tools add mcp looks up the service in the selected workspace before writing agent.toml. Use mason --profile <profile> tools add mcp <service> to select a workspace. A missing service or failed lookup (including authentication or permission errors) leaves the project unchanged. This checks service metadata access, not whether every tool can be executed at runtime. Removing local bindings does not require workspace access.

mason tools list
mason tools list --kind mcp
mason tools list --kind mcp --schema main.tools
mason tools list --kind sandbox
mason tools list --kind genie-one
mason tools list --kind genie-agent
mason --output json tools list

No agent project is required for discovery. MCP discovery uses your Databricks profile; the sandbox, uc-function, genie-one, and genie-agent kind filters show local recipes without authentication. --schema requires --kind mcp and replaces the default system.ai scope. An API/authentication failure returns nonzero and marks discovery incomplete, while retaining local recipes; it is not reported as an empty successful discovery. Listing metadata does not verify runtime execution permissions.

Migration: the former configured tools list view and its --source option are removed. Read agent.toml (its [[tools]] entries) to inspect configured bindings. Discovery JSON uses schema_version: 2, with available_tools (name, kind, add_command), mcp_schema (null for local-only recipes), complete, and errors. Replace old scripts that read configured-list JSON with TOML inspection. Replace mason mcp list [--schema catalog.schema] with mason tools list --kind mcp [--schema catalog.schema]; the former command is removed. Use mason tools list --help for the new discovery contract.

Read-only live discovery can be checked against the installed wheel without creating a project or deploying an agent:

MASON_E2E_PROFILE=<profile> .venv-functional/bin/pytest tests/e2e/tool_discovery_test.py -v

The live checks compare default and MCP-filtered discovery with the compatibility service list. Set MASON_E2E_SCHEMA=catalog.schema to exercise an additional schema. The installed CLI's local add/review/remove flows and all updated help pages are covered by tests/functional/cli_smoke_test.py.

In Mason-server templates, custom Python tools are code-first. Write them with the framework's native decorator in agent/tools/: LangGraph uses @tool, while OpenAI Agents uses @function_tool. The templates auto-discover decorated tools from that package and add them to the agent; there is no CLI command or agent.toml entry to keep in sync. Customer-managed MCP servers are likewise ordinary code in agent/mcps.py and are joined with the managed bindings by mcp_tools(...) or mcp_servers(...).

Projects created with --server custom do not auto-discover agent/tools/ or load managed tool bindings from agent.toml, so mason tools add rejects those projects. Wire framework-native Python tools and MCP servers directly in agent/agent.py instead.

If an older Mason-server manifest contains source = { kind = "python", ... }, remove that [[tools]] entry; the decorated tool in agent/tools/ remains active. mason dev and mason deploy do not generate or patch Python tool code, and do not alter the manifest's [[tools]] bindings.

Sandbox scopes default to read-only access. Repeat --scope to allow more than one resource, use volume: or workspace: for those resource types, and use --permission read_write only when the agent needs writes. Every sandbox call carries this fixed downscope in MCP _meta, outside the tool arguments controlled by the model.

Genie tools

Genie One and Genie Agent support ship with Mason, but bindings are opt-in, like sandbox tools. Installing Mason does not configure a Genie Space ID or enable a Genie binding. Add only the capabilities your agent needs:

mason tools add genie-one --name genie_one
mason tools add genie-agent SPACE_ID --name genie_agent
mason tools list --kind genie-one
mason tools list --kind genie-agent
mason tools remove genie_one
mason tools remove genie_agent

--name is optional and defaults to genie_one or genie_agent, respectively. Both add commands and remove accept --source PATH to select a project instead of the current directory. Discovery needs no project; read that project's agent.toml to inspect configured bindings. For scripted output, put the global -o json option before tools, as in mason -o json tools add genie-one --source ./my-agent. Adding a binding is offline: it updates agent.toml without contacting Genie or checking permissions. The corresponding sources are:

[[tools]]
id = "genie_one"
source = { kind = "genie_one" }

[[tools]]
id = "genie_agent"
source = { kind = "genie_agent", space_id = "<your-space-id>" }

Replace SPACE_ID or <your-space-id> with an existing space's 32-character lowercase hexadecimal ID. genie-one connects to the workspace-wide MCP endpoint https://<workspace-hostname>/api/2.0/mcp/genie, without a space suffix. genie-agent uses the native Genie Chat-mode conversation API through the Databricks SDK, not the streaming Agent-mode API or the per-space MCP endpoint.

Each native binding exposes {id}_ask, {id}_poll, and {id}_query_result, where {id} is its binding name. Ask accepts an optional conversation_id for follow-ups. Ask and poll share a 120-second budget per call, including client setup and submission. If the response is still running, they return timed_out with the conversation and message IDs so the caller can poll again. If submission times out before a message ID is received, ask returns INDETERMINATE_SUBMISSION: the request may still complete, so do not resubmit automatically. NOT_SUBMITTED means client setup timed out before sending the question. Query results include the first 100 rows, column schema, a truncation indicator, and a deep link to the conversation.

Both framework modules, databricks_mason.langgraph and databricks_mason.openai, export genie_tools(). New Mason-server templates use it automatically for native Genie Agent bindings; Genie One uses the existing managed MCP helpers. In an existing Mason-server project, import genie_tools from your framework module and add *genie_tools() to the agent's existing tool list. The CLI does not patch existing Python code.

Both paths use Mason's existing authentication and routed workspace. Genie One requires the Managed MCP Servers workspace preview; delegated access requires the genie OAuth scope. The effective caller needs access to the data, the SQL warehouse, and the selected Genie space where applicable. Mason does not grant permissions or promise a service-principal fallback when caller credentials lack access. An offline add succeeding does not establish runtime access.

The opt-in live tests exercise both frameworks against the configured workspace and an existing Genie space. From integrations/mason, with both framework extras installed:

DATABRICKS_CONFIG_PROFILE=my-workspace RUN_MASON_GENIE_TESTS=1 \
  MASON_GENIE_SPACE_ID=SPACE_ID \
  uv run pytest tests/integration_tests/genie_tools_test.py

By default they ask for the row count of samples.nyctaxi.trips. Set MASON_GENIE_QUESTION for another dataset and MASON_GENIE_EXPECTED_VALUE to assert a known result cell.

Initialize the chat app demo

The chat app is a LangGraph-specific init overlay, not a command that mutates an existing project. It is included by default for --framework langgraph; pass --disable-chat-app to scaffold the API-only backend instead.

mason init --framework langgraph \
  --profile <profile> \
  ./my-agent
cd ./my-agent
mason dev

The chat app includes synchronous, SSE streaming, background polling, Session Store, Memory Store, and HITL resume UI. The framework-specific overlay adds ui/, runtime/ui.py, the UI-enabled runtime/main.py, and UI tests.

For the full deployed demo, bind both managed stores, then deploy:

mason sessions bind mason-demo-sessions
mason memory bind mason-demo-memory
mason --profile <profile> deploy mason-agent-demo --source .

(bind declares the store name in agent.toml; mason deploy creates any declared-but-missing store and grants the app's service principal access to it. The memory store id flows to the runtime via the AGENT_MEMORY_STORE env var, injected by deploy and mason dev — it is not persisted in agent.toml.)

The chat UI generates a stable application session UUID in browser local storage, places it inside the invocation's opaque input, and creates a fresh invocation UUID per turn. The __Host-databricks-app-router cookie remains independent: API clients may reuse it for sticky replica routing, but it is neither authentication nor the template's application session state.

The generated README.md documents every request the client makes: config discovery, sync and SSE invocations, background submission and polling, session transcript loading, HITL resume, and memory entry operations. Capability colors are automatic from /api/demo/config; only the sync/streaming/background transport selector is manual.

Contributing

Developing Mason itself - the CLI, SDK/runtime, and templates - plus the local dev loop and how to test unreleased changes on mason dev and mason deploy, is covered in CONTRIBUTING.md.