app/agents/shared/ is a sibling of anonymous_chat/podcaster/multi_agent_chat/
video_presentation, so it should only hold code shared across 2+ of those
agents. In practice podcaster and video_presentation import nothing from it,
and anonymous_chat needs only context + compaction + retry_after + web_search.
Everything else was multi_agent_chat-only (the boundary just passes through).
Move the multi_agent_chat-only cluster into multi_agent_chat/shared/ (files
moved verbatim via git rename; ~116 import sites rewritten):
errors, feature_flags, filesystem_selection, path_resolver, prompt_caching,
sandbox, llm_config, mention_resolver
middleware/busy_mutex, middleware/kb_persistence
busy_mutex/llm_config/mention_resolver are boundary-only but import the moved
modules, so they were folded in to avoid a backwards shared -> multi_agent_chat
dependency. main_agent builders now import the impls directly; the shared
middleware barrel keeps only the genuinely-shared compaction + retry_after.
Also delete the dead leftover shared/plugins and shared/skills dirs (live
copies already live under main_agent/).
Remaining in app/agents/shared/: context, system_prompt(+prompts), checkpointer,
middleware/{compaction,retry_after,dedup_tool_calls}, tools/. checkpointer and
system_prompt are boundary-only infra pending a dedicated home decision.
permissions.py (authorization Rule/Ruleset model) is consumed across all
MAC subagents + the permissions middleware, with a single external
consumer (user_tool_allowlist service) -> move to
multi_agent_chat/shared/permissions.py and repoint all 42 sites.
deliverable_wait.py (wait_for_deliverable) is used only by the podcast and
video_presentation deliverable tools -> colocate into
subagents/builtins/deliverables/.
No behavior change; import-all + permission/allowlist/deliverable unit
tests stay green.
receipt.py (Receipt model + make_receipt) and receipt_command.py
(with_receipt Command helper) are a tight pair used only by MAC subagent
tools, the graph state, and the kb_persistence middleware -- no external
code imports them (the streaming tool_end handler only references them in a
docstring). Move both into a dedicated receipts/ package
(receipts/receipt.py + receipts/command.py) and repoint importers.
No behavior change; import-all + receipt/deliverable unit tests stay green.
Vertical-slice colocation: all main-agent code should live under
main_agent/ instead of being split across a parallel middleware/main_agent
tree. Move multi_agent_chat/middleware/main_agent/ -> main_agent/middleware/
and its assembler middleware/stack.py -> main_agent/middleware/stack.py, so
the main-agent slice is self-contained (graph, runtime, system_prompt, tools,
middleware).
Genuinely cross-slice middleware (middleware/shared/, middleware/subagent/)
stays under multi_agent_chat/middleware/ for a later slice; the moved builders
now reference it via absolute imports.
Pure move + import rewrite (git-tracked renames). Verified: full unit suite
green (2430 passed, 1 skipped), including test_import_all and the
checkpointed-subagent middleware suite.
Make create_multi_agent_chat_deep_agent the unconditional agent factory in
all three streaming entry points (stream_new_chat monolith + new_chat/resume
flow orchestrators). Drop the MULTI_AGENT_CHAT_ENABLED branch and the now-unused
create_surfsense_deep_agent / _app_config imports. The single-agent
implementation (chat_deepagent.py, subagents/) is left in place; it is deleted
in a later phase. Suite green (2758 passed).
Relocate the entire new_chat/middleware/ package to the shared kernel as one
cohesive unit (it is live shared infrastructure: the multi-agent stack wraps
nearly every middleware via multi_agent_chat/middleware/main_agent/*, and
anonymous_agent consumes it too). Flip 69 live importers across both the
package-path and submodule-path forms.
Shims left for the frozen single-agent stack: a package __init__ re-export plus
submodule shims for permission, skills_backends, and scoped_model_fallback
(the three imported via submodule path by chat_deepagent/subagents).
Cycle break: importing shared.middleware previously reached back into
new_chat.tools at module load, which dragged in new_chat.__init__ ->
chat_deepagent -> the middleware shim -> half-initialized shared.middleware.
Made action_log's ToolDefinition import TYPE_CHECKING-only and
tool_call_repair's INVALID_TOOL_NAME import function-local. These tools-package
back-edges fully resolve in slice 6.
Asset note: skills_backends._default_builtin_root now walks to
app/agents/new_chat/skills/builtin (the skills/ tree migrates in slice 7).
Two independent leaf modules (no intra-new_chat deps, no frozen importer),
consumed only by flows/routes/tests. Flipped 8 importers across both the
dotted-path and module-style (from app.agents.new_chat import mention_resolver)
forms. No shims needed.
Relocate the mutually-dependent LLM config layer and the LiteLLM prompt-caching
helper to the shared kernel as one unit, rewiring their internal cross-reference
to the shared paths. Flip 21 non-frozen importers. Re-export shims remain at
new_chat/{llm_config,prompt_caching}.py for the frozen single-agent stack
(chat_deepagent); they will be removed when that stack is retired.
Relocate three leaf filesystem-cluster modules to the shared kernel and flip
all 38 importers. No re-export shims needed (no frozen single-agent importer).
This also resolves the pre-existing shared->new_chat back-edge from
shared/receipt_command.py onto filesystem_state.
filesystem_backends is intentionally deferred to slice 5: it depends on
new_chat middleware (kb_postgres_backend, multi_root_local_folder_backend)
that have not yet moved, so relocating it now would create a shared->new_chat edge.
Promote the filesystem mode contracts (FilesystemMode, FilesystemSelection,
ClientPlatform, LocalFilesystemMount) out of `new_chat` into the cross-agent
`app/agents/shared` kernel.
Pure leaf consumed across the whole multi-agent filesystem middleware/tool tree,
the chat flows/monolith, routes and tests. git mv (content unchanged) + flipped
all ~48 importers. A re-export shim remains at new_chat/filesystem_selection.py
only for the not-yet-retired single-agent (chat_deepagent).
Also updated the stream parity test's annotation normalizer to strip the new
app.agents.shared.filesystem_selection. prefix (the dataclasses' __module__
changed with the move), keeping monolith<->flows signature parity intact.
Behavior-preserving: only import paths change. 1326 tests green.
Continue promoting the shared agent toolkit out of `new_chat` into the
cross-agent `app/agents/shared` kernel.
- state_reducers.py: clean move (no single-agent importer); all 7 importers
flipped to app.agents.shared.state_reducers.
- context.py: moved to app.agents.shared.context; flipped the multi-agent,
app, automations, chat-flows and monolith importers. A thin re-export shim
remains at new_chat/context.py because the not-yet-retired single-agent
(chat_deepagent) and the new_chat package __init__ still import it; the shim
goes away with the single-agent deletion.
- Updated the stream parity test's annotation normalizer to strip the new
app.agents.shared.context. prefix (SurfSenseContextSchema.__module__ changed
with the move), keeping monolith<->flows signature parity intact.
Behavior-preserving: definitions unchanged; only import paths move. 1219 tests green.
First slice of promoting the shared agent toolkit out of the misnamed
`new_chat` package into the cross-agent `app/agents/shared` kernel.
`errors.py` is a leaf module (no intra-package deps) consumed by the
multi-agent chat, the chat streaming flows/monolith, and tests — i.e. it is
shared infrastructure, not single-agent code. Moved it verbatim to
`app.agents.shared.errors` and flipped all 12 importers. No re-export shim
remains since zero importers needed it.
Behavior-preserving: identical class/enum definitions; only the import path
changes. 1208 agent + chat-task tests green.
- Deleted the `search_surfsense_docs` tool and its associated files, streamlining the agent's toolset.
- Updated various components and prompts to remove references to the now-removed tool, ensuring consistency across the codebase.
- Adjusted documentation to direct users to the SurfSense documentation link for product-related queries instead.
- Added new environment variables for controlling task execution limits, including `SURFSENSE_SUBAGENT_INVOKE_TIMEOUT_SECONDS`, `SURFSENSE_TASK_BATCH_CONCURRENCY`, and `SURFSENSE_TASK_BATCH_MAX_SIZE`.
- Updated documentation to reflect new batch processing capabilities for `task` calls, allowing for concurrent execution of multiple subagent tasks.
- Improved error handling and receipt generation for deliverables, ensuring consistent feedback on task status.
- Refactored middleware to incorporate search space ID for better task management.
Slim composition root for the resume-chat streaming flow. Mirrors the
new_chat orchestrator but specialized for resumed turns:
* no fresh user turn, no title generation, no image-capability gate
* persists a fresh assistant shell for the resumed turn
* applies build_resume_routing to dispatch user decisions to the
correct paused subagent before invoking the agent
* shares the same stream_loop + flow-local _recover closure for in-
stream provider rate-limit recovery
Also lands flows/__init__.py, which becomes the public chat-flow API:
from app.tasks.chat.streaming.flows import stream_new_chat, stream_resume_chat
Existing wiring (routes, contract test) still imports from the legacy
app.tasks.chat.stream_new_chat module. Cutover is the next phase.
Three focused modules used by the upcoming resume-chat orchestrator:
* runtime_context: build_resume_chat_runtime_context assembles the
SurfSenseContextSchema for a resume turn (handles empty mention
lists, since resume requests do not carry fresh @-mentions).
* assistant_shell: persist_resume_assistant_shell writes a fresh
assistant row for the resumed turn so the post-stream finalize
has a target.
* resume_routing: build_resume_routing collects the pending
interrupts across paused subagents and slices the flat list of
ResumeDecision[] into the correct (thread, subagent) buckets so
LangGraph routes each decision back to the right paused tool call.
Add-only; no orchestrator yet (next commit).
Slim composition root for the new-chat streaming flow. Sequences:
1. validate inputs and load the LLM bundle (negative id => YAML)
2. open the OTEL chat_request span; set agent_mode tag
3. spawn the four pre-stream DB writes (set-ai-responding, persist
user turn, persist assistant shell, first-assistant probe)
4. reserve premium quota (with free-fallback retry on denial)
5. build connector + checkpointer + agent + input_state
6. emit first frames (message-start, step-start, initial thinking step)
7. spawn the background title generator
8. run the shared stream_loop with a flow-local _recover closure that
reroutes to the next auto-pin config on provider 429s
9. finalize: emit terminal title/token frames, shielded assistant
finalize, release-or-finalize premium quota, close session, GC,
record OTEL outcome
Public entry-point flows/new_chat/__init__ re-exports stream_new_chat.
Existing wiring (routes, tests) still imports the legacy function from
app.tasks.chat.stream_new_chat. Cutover is a later commit.
Seven focused modules that the upcoming new_chat orchestrator
composes:
* auto_pin: resolve_initial_auto_pin selects the initial config (with
vision-capable filtering and error classification).
* llm_capability: check_image_input_capability blocks routing an
image-bearing turn to a known text-only model.
* runtime_context: build_new_chat_runtime_context assembles the
SurfSenseContextSchema for a new-chat turn.
* persistence_spawn: spawn_set_ai_responding_bg, spawn_persist_user_task,
spawn_persist_assistant_shell_task, and await_persist_task background
the four pre-stream DB writes so they overlap with agent build.
* initial_thinking_step: build_initial_thinking_step +
iter_initial_thinking_step_frame produce the very first thinking-1 SSE
step ("Understanding your request" / "Analyzing referenced content").
* title_gen: spawn_title_task + maybe_emit_title_update +
await_pending_title_update background the thread-title generator and
interleave its update into the stream when ready.
* input_state: build_new_chat_input_state assembles the LangGraph
input_state (history bootstrap, mentions resolution, context blocks,
human-message construction). The heavy one.
Add-only; no orchestrator yet (next commit).
Extracts finalize_assistant_message: the post-stream server-side write
of the final assistant message (with content parts + token usage)
guarded by asyncio.shield + shielded_async_session so a client
disconnect cannot abort the persist.
Add-only; legacy stream_new_chat.py keeps its inline finalize block
until cutover.
Two cooperating modules that wrap stream_agent_events with in-stream
recovery from provider 429s:
* rate_limit_recovery: can_recover_provider_rate_limit truth-table
guard, reroute_to_next_auto_pin (selects the next eligible auto-pin
config and reloads the LLM bundle), log_rate_limit_recovered.
* stream_loop: run_stream_loop drives stream_agent_events in a
while-True loop, delegating recovery to a flow-supplied RecoverFn
callback so new_chat and resume_chat can share the same loop while
keeping their own nonlocal state.
Add-only; not yet wired into any orchestrator.
Extracts handle_terminal_exception: the shared except-branch behavior for
the chat orchestrators. Classifies the raised exception, logs the
structured chat_stream error event, and emits the terminal-error SSE
frame + done sentinel via the streaming service.
Add-only; nothing imports it yet.
Centralizes the premium-credits lifecycle for chat turns:
* needs_premium_quota: gate check (premium user + non-fallback config).
* PremiumReservation: dataclass capturing reservation state + token totals.
* reserve_premium / finalize_premium / release_premium: idempotent
reservation, commit, and rollback used by the orchestrators.
Add-only; legacy stream_new_chat.py keeps its inline quota handling
until cutover.
Six small, single-purpose modules shared by the upcoming new_chat and
resume_chat orchestrators:
* llm_bundle: dispatches negative config_id to the YAML loader and
non-negative config_id to the DB loader, returning (llm, AgentConfig).
* pre_stream_setup: builds the connector service, resolves the
Firecrawl API key, and returns the chat checkpointer.
* first_frames: iter_initial_frames + iter_final_frames emit the canonical
message-start / step-start / idle / finish / done SSE envelope.
* finalize_emit: iter_token_usage_frame emits the per-turn usage frame
from a TokenAccumulator summary.
* finally_cleanup: close_session_and_clear_ai_responding and run_gc_pass
centralize the finally-block bookkeeping.
* span: open_chat_request_span / set_agent_mode / close_chat_request_span /
record_outcome_attrs wrap the OpenTelemetry chat_request span.
Add-only; these are not yet wired into stream_new_chat.py.
Extracts the inner agent-streaming driver previously inlined as
_stream_agent_events in stream_new_chat.py.
stream_agent_events drives graph_stream.event_stream.stream_output and,
after the agent finishes, performs the post-stream safety-net work:
* commit any pending content the agent never explicitly finished
* evaluate file-operation contract outcomes and emit the appropriate
contract verdict for desktop_local_folder turns
This unit is what flows/shared/stream_loop.py wraps in the rate-limit
recovery while-loop. Add-only; no existing wiring uses it yet.
Extracts the agent-construction wrapper that the chat streamers call to
materialize the LangGraph agent for a given thread. Centralizes how we
pass the agent factory plus checkpointer, runtime context, and the
in-memory content builder.
Add-only; pre-existing inline equivalent in stream_new_chat.py stays
until cutover.
Extracts the desktop_local_folder file-operation contract helpers:
* contract_enforcement_active: gates the contract on filesystem mode.
* evaluate_file_contract_outcome: scores tool outputs as success/no-op.
* log_file_contract: structured logging of contract verdicts.
This is the unit responsible for catching agents that claim to have
written/edited a file without actually invoking the filesystem tool.
Add-only; stream_new_chat.py keeps its inline duplicates until cutover.
Extracts two pure context helpers used during input-state assembly:
* mentioned_docs.format_mentioned_surfsense_docs_as_context: renders the
user's @-mentioned SurfSense docs into the LLM context block.
* deepagents_todos.extract_todos_from_deepagents: pulls the in-progress
todo list from a deep-agents state snapshot for the title generator.
Add-only; existing call sites in stream_new_chat.py remain untouched
until cutover.
Foundation layer for the parallel refactor of stream_new_chat.py.
Extracts the StreamResult dataclass (tracks per-turn streaming state)
and a small set of shared utilities (resume_step_prefix, safe_float).
Add-only; no existing code imports from this package yet. Existing
stream_new_chat.py keeps its inline equivalents until cutover.
