Single tool exposed to the main agent. The main agent passes a natural-language
`intent`; a focused drafter sub-LLM turns it into a full AutomationCreate JSON;
that JSON is surfaced via request_approval (action_type "automation_create") so
the user can edit/approve it on a frontend card; on approval the tool persists
via AutomationService. Three phases, one tool call.
Scope split:
- main agent sees only `intent: str` (no schema knowledge leaks into the calling
graph) — prompt fragments scoped accordingly.
- drafter sub-LLM owns the schema + few-shot intent→JSON examples — lives in
the generating graph's prompt (tools/automation/prompt.py).
Files:
- main_agent/tools/automation/{create.py, prompt.py, __init__.py}: new tool
+ drafter system prompt with two few-shot intent→JSON examples.
- system_prompt/prompts/tools/create_automation/{description.md, example.md}:
intent-only guidance for the main agent.
- main_agent/tools/index.py: add create_automation to the main-agent allowlist.
- new_chat/tools/registry.py: deferred-import factory to break the
multi_agent_chat ↔ registry cycle; one ToolDefinition entry.
The citations fix (cacb27e0) added a "Chunk citations in your prose"
section to system_prompt_desktop.md telling the KB subagent to always
leave `evidence.chunk_ids` null and emit no `[citation:...]` markers in
desktop mode, but left the pre-existing line declaring that
`chunk_ids` apply to `<priority_documents>` hits. The two rules
contradicted each other; the model picked one per turn.
Strike the stale conditional clause and point at the dedicated section
as the single source of truth. Matches the parallel line in
system_prompt_cloud.md and the already-consistent
system_prompt_readonly_desktop.md.
Resolves: surfsense_backend/app/agents/new_chat/middleware/memory_injection.py
- Took both imports: upstream moved MEMORY_HARD_LIMIT/SOFT_LIMIT to
app.services.memory; kept our perf-logger import for timing.
Pulls in upstream changes:
- Memory document feature (services/memory refactor, removal of
app.agents.new_chat.memory_extraction and background extraction in
stream_new_chat — agent now drives memory via update_memory tool).
- BACKEND_URL env refactor across web tool-ui/editor/chat/dashboard/lib.
- GitHub Actions backend test workflow + pre-commit biome bump.
- Token-display polish in MessageInfoDropdown; save_memory no-update
sentinel.
Verified: 1723 unit tests pass, ruff clean. No semantic regression in
stream_new_chat (their memory-extraction deletion and our preflight
removal touch different functions).
Adds an optional planner LLM role wired through KnowledgePriorityMiddleware
so KB query rewriting, date extraction, and recency classification run on a
cheap model (e.g. gpt-4o-mini, Haiku, Azure nano) instead of the user's
chat LLM. Operators opt in by setting is_planner: true on exactly one
global config; without it, behavior is unchanged.
Only MCP tools have a persistence target for 'approve_always' (the
connector's trusted-tools list); for native tools the decision lives
only in the in-memory runtime ruleset. Reflect that in the wire palette
so the FE can stay a pure renderer of allowed_decisions instead of
peeking at context.mcp_connector_id to decide whether to show the
'Always Allow' button.
The backend still accepts an 'approve_always' reply for any tool kind
(in-memory promotion is harmless), it just doesn't advertise it when
there's nowhere to persist.
Renames the SurfSense HITL extension decision-type from "always" to
"approve_always" so it sits in the same verb-first family as "approve",
"reject", and "edit". The Python constant is now SURFSENSE_DECISION_APPROVE_ALWAYS;
the wire value, the permission-domain decision_type, and the FE union members
all match (no wire/internal mismatch).
Both the multi_agent_chat permission middleware and the legacy new_chat one
accept the new wire value; the FE types.ts union is updated accordingly.
The "context.always" payload key is intentionally left untouched - it's the
patterns-to-promote field, semantically distinct from the decision type.
Until now an "Always Allow" reply only updated the in-memory runtime
ruleset, evaporating after the session ended. Persist it to the
existing connector.config['trusted_tools'] list so the next session's
fetch_user_allowlist_rulesets picks it up and the user is never asked
again for the same (connector, tool) pair.
- TrustedToolSaver + make_trusted_tool_saver(user_id) in
user_tool_allowlist: opens its own session via async_session_maker
per call, logs and swallows failures (in-memory promotion is the
canonical "always" path, durable persistence is opportunistic).
- PermissionMiddleware._process is now pure: returns
(state_update, list[_AlwaysPromotion]). aafter_model awaits the
saver for each promotion; after_model discards them. Promotions are
only emitted for tools whose metadata exposes mcp_connector_id, so
native tools and KB FS ops are correctly skipped.
- main_agent factory builds the saver once per turn and stashes it in
dependencies["trusted_tool_saver"]; pack_subagent and the KB
middleware stack forward it through build_permission_mw.
- Renamed pm._process(state, None) call sites in two existing tests to
pm.after_model(state, None) so they exercise the public hook
contract instead of the now-tuple-returning private method.
The FE permission card needs mcp_connector_id, mcp_server, and
tool_description in the interrupt context to render "Always Allow"
against the right connected account. Thread the tool through the
ask pipeline:
- pack_subagent → build_permission_mw(tools=...) → PermissionMiddleware
(tools_by_name) → request_permission_decision(tool=...) →
build_permission_ask_payload(tool=...) projects card fields out of
BaseTool.
- mcp_tool.py: stdio path now stashes mcp_connector_id in metadata for
parity with the HTTP path.
