[mason] Per-user memory/session isolation + agent.toml store bindings - #533
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Long-term memory and the durable session store were keyed by a single static actor (AGENT_*_ACTOR_ID env, default "agent"), so every user of a deployed agent shared one memory/session namespace — a privacy and relevance problem for multi-user apps. Make the actor the request's signed-in user instead: - The SDK adapters take actor as an explicit parameter — memory_tools(actor), session_store(session_id, actor), thread_config(session_id, actor). The memory tools close over the actor (it is NOT a tool argument the model can set/spoof). - The templates resolve the actor per request from the forwarded-identity header (X-Forwarded-Email) in runtime.py and pass it through agent.py's _actor(); it falls back to "agent" locally. The demo UI's state routes use the same actor so its memory/session panels show the signed-in user's own data. - Drop the now-dead AGENT_*_ACTOR_ID env vars and `mason deploy --actor-id` (the actor is no longer a static deploy-time value). The low-level `mason memory entries` / `mason sessions` --actor-id args are unchanged (direct store access). Both templates (openai + langgraph). Tests + docs updated; per-user actor is invisible to agent authors — they call the same memory_tools()/session_store(). Co-authored-by: Isaac <no-reply@databricks.com>
Store bindings become part of the agent manifest instead of only living as app.yaml env. `mason memory bind <store>` / `mason sessions bind <store>` (+ unbind) declare a `[memory_store]` / `[session_store]` table in agent.toml, creating the managed store if it doesn't exist. The runtime resolves the store name explicit-arg > AGENT_*_STORE env > agent.toml binding > none — the conventional precedence (env overrides the committed default), read via a new runtime.tool_manifest.store_binding() so a bind takes effect with no env plumbing. Co-authored-by: Isaac <no-reply@databricks.com>
Store create/resolve is a network round-trip that can stall (the agents/v1 create endpoint is slow and occasionally drops the response), so `mason memory/sessions bind` and `stores create` looked hung. Add a `render.status()` spinner around those calls. Co-authored-by: Isaac <no-reply@databricks.com>
The scaffolds need the per-user-actor + agent.toml store-binding runtime (memory_tools(actor), resolve_*_store, thread_config(actor)), which ships in 0.1.3.dev0. Bump both template pins so a scaffold resolves a package new enough to read its bindings. The package version bump itself lands separately. Co-authored-by: Isaac <no-reply@databricks.com>
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Two related changes to how a mason agent resolves its memory/session config.
Per-user isolation (
e2b044d): managed memory and session data now partition by the signed-in user (X-Forwarded-Email) instead of one shared"agent"actor, so a deployed multi-user agent no longer pools everyone's memory/transcripts. The actor is a factory parameter closed over in the tools — not a tool argument — so the model can't set or spoof it. Falls back to"agent"locally.Store bindings in agent.toml (
56b5113):mason memory bind <store>/mason sessions bind <store>(+unbind) declare a[memory_store]/[session_store]table in agent.toml, creating the store if needed. The runtime resolves the store name explicit arg →AGENT_*_STOREenv →agent.tomlbinding → none (conventional precedence; env overrides the committed default). A bind takes effect with no env plumbing.Both templates + adapters updated; app.yaml unchanged. Tests: 231 unit, 21 openai + 25 langgraph scaffold; ruff + ty clean.
Tested e2e dev on
e2-dogfood.