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.
Connector kb_sync_services (gmail, onedrive, google_calendar, jira),
streaming indexers (discord, luma, teams) and the file-processor save
path all called embed_text inside async coroutines, blocking the
background worker's event loop for the duration of the embed. Wrap each
call site in asyncio.to_thread so concurrent indexing tasks stop
serialising on the embed.
_restore_in_place_document and _reinsert_document_from_revision are
async helpers invoked by the synchronous-feeling POST /api/threads/.../revert
route; both ran embed_texts inline, blocking the event loop while the
HTTP client waited.
LiteLLM normalizes every provider's cache fields onto
usage.prompt_tokens_details (cached_tokens + cache_creation_tokens).
The earlier fallback to usage.cache_read_input_tokens /
usage.cache_creation_input_tokens was wrong: Anthropic-shaped fields
only live there via a trailing setattr loop, and the canonical field
name on the wrapper is cache_creation_tokens (not _input_tokens).
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.
Build and Push Docker Images / tag_release (push) Waiting to run
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- Added a new function `_warm_agent_jit_caches` to pre-warm agent caches at startup, reducing cold invocation costs.
- Updated the `SurfSenseContextSchema` to include per-invocation fields for better state management during agent execution.
- Introduced caching mechanisms in various tools to ensure fresh database sessions are used, improving performance and reliability.
- Enhanced middleware to support new context features and improve error handling during connector and document type discovery.
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- Updated `litellm` dependency version from `1.83.4` to `1.83.7`.
- Adjusted `aiohttp` version from `3.13.5` to `3.13.4` in the lock file.
- Implemented `apply_litellm_prompt_caching` in `chat_deepagent.py` to improve prompt caching.
- Added model name resolution logic in `chat_deepagent.py` to ensure correct provider-variant dispatch.
- Enhanced `llm_config.py` to configure prompt caching for various LLM providers.
- Updated tests to verify correct model name forwarding and prompt caching behavior.