The per-bot tool policy now carries the main bot's own tools: delegate_task
and update_user_profile are the main-only builtins, so the separate filters
from the profile and delegation branches are gone. One set of exposed tool
names drives both the executor's call guard and the prompt. The schedule
tool says new routines default to the user's time zone, which makes the
extra prompt line unnecessary. Tests that relied on an empty tool list
meaning every tool, or on the tool's own refusal text, now pass the tool
explicitly or expect the executor's refusal.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
Every bot saw every builtin tool, and nothing outside the runtime's own
declaration kept a run to the tools it was given: the executor ran any tool a
runtime asked for, and the Pi runtime exposed all builtins whenever a caller
passed an empty tool list, which the safety checker, the history summarizer
and the team chat judge do on purpose. A specialist could not be kept to the
tools of its lane, bots had no capability tags, and the main bot could only
schedule routines for itself.
- A bot can carry a builtin tool allowlist. Its runs see only the allowlisted
builtins, and an empty allowlist means none. MAIN_ONLY_BUILTIN_TOOLS (empty
until the user profile and delegation tools land) never reach other bots.
applyTool refuses any tool the run did not expose, and an empty tool list
now means no tools for the Pi runtime and its subagents.
- A bot spawned by a restricted bot inherits the restriction and a duplicate
keeps it, so neither path widens a bot's tools.
- bots.create and bots.update accept tags, restrictBuiltinTools and
allowedBuiltinTools; the router rejects names that are not builtin tools.
bots.builtinTools lists the names a bot's allowlist can use.
- Bot settings get a Tags field and a switch with a tool checklist under
Advanced.
- The main bot's schedule_create takes an optional bot: one of the user's
bots in any space the user still belongs to. The routine belongs to that
bot in its space and wakes in the main conversation when the bot is a
member there, otherwise in its 1:1 thread. Routines record the bot that
scheduled them (additive migration), so schedule_list and schedule_cancel
also cover exactly the routines a bot scheduled for others. Cancelling by
a name that matches several routines is refused.
Tested with unit tests for the per-bot filter, the applyTool guard, the empty
tool list, and on-behalf resolution, creation, listing and cancelling; with
Postgres journeys for on-behalf routines waking across spaces and in the main
conversation and for allowlist enforcement through the scripted runtime; and
with a web E2E for the settings section.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
Why: the user profile only knew what onboarding wrote, so people had to repeat themselves,
and semantic recall needed a hosted or separately run memory service. The profile now grows
from everyday conversations, and recall can run on the user's own server with a local
embeddings endpoint (for example llama.cpp). Both stay optional and are tested offline.
What:
- adapter-kit: EmbeddingProvider contract (describe, embed) and the profile.consolidate job
(payload schema and a replace-keyed job factory, one pending job per user).
- adapters: OpenAI-compatible embeddings (POST {baseUrl}/embeddings through the guarded
OpenAI-compatible fetch, batching, bounded responses, clear errors), a deterministic
emulator, an in-process /v1/embeddings server emulator, and a shared conformance suite.
- adapters: "builtin" semantic memory stored in Postgres (semantic_memory_items with Float[]
embeddings, cosine ranking in process, bot/shared/history scopes matching the existing
providers), registered in the memory provider factory and configured through
memory.connectProvider with an embeddings URL, model and optional API key. Private
endpoints require the deployment owner, like the other local memory providers.
- adapters: profile consolidation reads the user's settled app conversations since
UserProfile.consolidatedAt (never messaging channels), asks the main bot's model for strict
JSON facts and an updated summary, drops restatements (embedding cosine when built-in
memory is configured, normalized text otherwise), stores sensitive categories as pending,
and advances the cursor with a compare-and-set. A reconciler hook enqueues it at most daily
per active user; it skips under the scripted runtime and without a usable model.
- db: additive migration 20261002210400_ws4_semantic_memory.
- web: a "Built-in" option in Memory settings; AI data sharing names the embeddings server.
Tests: new embedding conformance, OpenAI-compatible wire, built-in provider and consolidation
unit tests; new Postgres suites for built-in memory (including a real loopback embeddings
server through the memory provider resolver) and for consolidation and its scheduling query,
both added to the integration harness. The settings E2E spec now opens the new form.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
The product needs one main agent that leads a main conversation, knows the
user across spaces, and hands work to visible specialists. Today none of
that exists in the data model: "Chief" is a client-side naming convention,
the "Hauptthread" is a group the web client creates by name, and nothing
stores what the agents know about the user.
This adds the shared foundation the Phase 1 features build on:
- Bot.kind ("main" | "specialist", one main bot per user) plus capability
fields (tags, builtin tool allowlist, model tier).
- ChatGroup.kind ("group" | "main", one main conversation per user) and
ChatGroup.leadBotId.
- UserProfile and UserFact with category, confidence, source, consent
status and per-space visibility; embeddings are stored as float arrays
because none of the Postgres images ship pgvector.
- DelegatedTask for work the main bot hands to a specialist.
- SpaceModelTier to map a model tier to a connected model per space.
The migration backfills each user's onboarding bot in the home space as the
main bot and turns an existing "Hauptthread" group containing it into the
main conversation. CHECK and foreign key constraints on existing tables are
added NOT VALID and validated in a separate migration, as elsewhere in this
repo.
New db helpers ensure the main bot and the main conversation (idempotent and
race-safe, repairing a lost lead), add same-space specialists to the main
conversation and announce it with a group.updated event, and load the user
context a run may see. The main conversation is exempt from the two-member
floor and cannot have its members replaced through a group update. The
contracts expose the new fields and the user model and delegated task
schemas.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
Extend the existing NewSpaceDialog project flow so an optional host
folder becomes the space workspace root. When .git metadata is present,
remote URL, default branch, and head commit are stored on the space.
Team Computer file APIs expose the linked folder under project/ without
a second VCS stack. Desktop adds a native folder picker; web falls back
to an absolute path field.
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: cynarAI <cynarAI@users.noreply.github.com>
The executor calls finalizeRun with toolCallBudgetCount and
toolCallBudgetExceeded in the updateMany data clause, but these
fields were missing from the Prisma schema, causing an Invalid
updateMany invocation error that blocked all runs from completing.
Also updated CLAUDE-RESUME-NOW with full infrastructure status.
