* fix(subagents): keep the subagent system prompt through context compaction
A subagent carries its whole system prompt (role, report contract, skills,
deferred tools) as the leading SystemMessage in state, because its agent is
built with system_prompt=None. Summarization only rescued tagged reminders and
the latest user message, so the first compaction summarized the prompt away
and every later model call in that subagent ran without its instructions.
Rescue system messages as well. The lead agent's prompt is not in state, and
its in-state SystemMessages are the tagged reminders already rescued, so the
lead chain is unchanged.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(compaction): clarify preservation contract and trim agent guidance
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Summarization rescues the latest user message by id so the current
request survives compaction (#4882). It picked that message with
is_real_user_message, which rejects every hide_from_ui HumanMessage,
including Human Input Card replies. When a run starts from a card
reply, the older visible request was kept and the user's answer was
summarized away.
Use is_genuine_user_message, which accepts hidden messages that carry a
valid human_input_response and still skips other hidden injections.
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* feat: add opt-in task notes and compacted history recall
* fix: validate task continuity state and preserve user answers
Honor explicit opt-out, preserve clarification replies and capture failure statuses, validate notebook writes, and clear branch archive references. Update the config version and audit optional LLM credentials, with regression and integration evidence.
* fix: align Helm config version with task continuity schema
* fix: preserve mixed task history and declare continuity policies
* fix: recover malformed history and evict archives atomically
* fix(summarization): resolve fraction triggers from declared context_window, degrade instead of crashing the agent build
A fraction trigger/keep clause requires profile["max_input_tokens"], which any
third-party OpenAI-compatible model lacks, so SummarizationMiddleware
construction raised ValueError out of create_summarization_middleware and failed
the whole agent build (#3103).
- factory: translate a declared model context_window into the langchain
profile (metadata-only, never reaches the provider payload); explicit
caller/override profiles win
- summarization factory: drop unusable fraction trigger clauses (absolute
clauses survive), fall a fraction keep back to the messages default, and
disable compaction with an actionable warning only when no usable trigger
clause remains — the agent build never dies from summarization config
- docs: config.example.yaml, ModelConfig.context_window, summarization.md
* refactor(summarization): share the default keep constant with the fraction fallback
The fraction-keep degradation fallback hardcoded ("messages", 20),
duplicating SummarizationConfig.keep's default_factory literal. Move the
value to a shared DEFAULT_KEEP constant so the two cannot drift apart.
* fix(summarization): keep trigger-null + fraction-keep constructing after degradation
A trigger of None with a fraction keep hit the all-clauses-dropped branch
(has_usable_trigger=False) and disabled compaction, and the accompanying
warning claimed configured triggers were all fraction-based when none were
configured. Only report nothing-usable when trigger clauses actually
existed; trigger:null keeps constructing the never-firing middleware with
the degraded keep, matching its behavior outside the degradation path.
* fix(summarization): address review — keep manual compaction, validate ContextSize, pin wiring
Review follow-ups on #4901:
- When every configured trigger is a dropped fraction clause, keep
constructing the never-firing middleware (trigger=None) instead of
returning None: manual /compact runs with force=True and never consults
trigger clauses, so it must keep working for a profile-less model
rather than reporting 'compaction is disabled'. The warning now says
auto-compaction will not fire while manual compaction remains.
- ContextSize gains a config-load validator: fraction values must be in
(0,1] (a percent-style 80 instead of 0.8 previously produced a threshold
the context could never reach — a silently inert trigger), absolute
values must be positive.
- New un-monkeypatched integration test pins the shipped wiring
(context_window declared -> real factory attaches profile -> fraction
clause survives -> middleware constructs), which the stubbed
middleware-side tests and kwarg-capturing factory-side tests each
stopped short of.
- Docs (summarization.md + config.example.yaml) clarify that the fraction
resolves against the summary/anchor model's context_window
(summarization.model_name when set, else the run model), including the
mismatch caveat for a larger-window summary model.
* fix(summarization): reject non-finite ContextSize values at config load
YAML .nan / .inf pass pydantic's float parsing, and nan <= 0 is False,
so the positivity check alone let them through as dead thresholds
(count >= nan is always False) — the same silent-inert-trigger class the
range validator was added to close. Guard with math.isfinite first,
consistent with the existing non-finite guards on mem0 timeout_seconds
and poll_after_seconds.
* fix(summarization): merge context_window into inferred profile, require whole message counts
- construct the model first, then merge max_input_tokens into the
provider-inferred langchain profile: passing profile= to the
constructor replaced the whole inferred metadata (tool_calling,
structured_output, io capabilities, output limits) with the single
key. An explicitly configured profile is still never clobbered.
- reject non-integral ContextSize values for type=messages at config
load: langchain slices the message list with them, so a float index
raised TypeError mid-compaction.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(extensions): let an out-of-tree extension observe what the agent did
DeerFlow's extension system can contribute middleware, services and routes,
but an extension cannot answer basic questions about a run without reaching
into host internals. Several of the facts it would need are destroyed by the
operations that produce them:
* The middleware chain injects and rewrites a lot of context — date
reminders, recalled memory, compaction summaries, durable-context data,
image payloads, activated skill bodies. Downstream, none of it is
attributable: at the model-call boundary an injected HumanMessage is
indistinguishable from the user's own, and anything wanting to tell them
apart has to pattern-match prompt wording, which breaks on the next copy
edit.
* Two runs of "the same agent" are only comparable if the chain enforced the
same limits, prompts and thresholds. Recovering that from outside means
reading private attributes and guessing which of them change behaviour — a
guess that rots silently as middlewares gain fields.
* The lead-agent factory resolves a model after runtime overrides, renders a
prompt, filters tools through authorization and composes a stack, all
inside one synchronous call, and none of it survives: a middleware sees its
neighbours but not the prompt, the run worker sees a graph but not what
went into it.
* Summarization is destructive by design. N messages leave the context and
one summary enters it; afterwards only the summary exists, so "which
messages became this?" is not reconstructible.
This adds seven neutral facilities so those facts are recorded where they are
still true, and releases the contract package as 0.2.0.
Message provenance
Producers stamp `deerflow_content_kind` / `deerflow_producer_kind` onto the
messages they inject or rewrite. Stamping is unconditional — a fact whose
presence depends on whether an observer is installed is not a fact — and the
keys are server-owned, so provenance cannot be forged from a request.
Middleware self-description
Twelve middlewares declare their own behaviour-affecting parameters through
a duck-typed `release_policy_parameters()`. Long text is hashed rather than
embedded: a declaration is an identity, not a copy of the prompt.
Agent assembly descriptor
`assemble_lead_agent()` returns the graph plus a descriptor whose fingerprint
answers "did anything about this agent change between these two runs?".
`make_lead_agent()` keeps its graph-only signature — it is the LangGraph
Server ABI declared in langgraph.json. Tools and skills are sorted before
hashing because their assembly order is incidental; middlewares are not,
because stack order decides what wraps what. Host build identity is reported
but excluded from the fingerprint, so a redeploy does not invalidate every
agent's identity.
Context compaction observation
Summarization emits the content hashes of the messages it is about to remove
joined to the summary that replaced them. Content is the only identity
available at that seam: the summary does not become a message, and what later
projects it into a request renders it bounded and escaped rather than
verbatim.
Neutral policy, transform and MCP-source facts
Guardrail decisions are published to runtime context under a `__`-prefixed
key; result-rewriting middlewares append a declared, ordered transform trail;
MCP tools carry their credential-free logical origin.
Extension route identity
Contributed routes are session-authenticated and cannot opt out, but
"logged in" and "administrator" are different questions. Extensions get a
neutral projection of the caller rather than the host's auth context, and
`require_admin` fails closed when identity cannot be determined.
Extension-owned tables
An extension that persists data owns its own MetaData and migration chain, so
its tables are absent from Base.metadata and `alembic revision --autogenerate`
proposes dropping them. Extensions declare a table prefix, which is rejected
at registration if it would shadow a host table.
The contract package stays dependency-free and imports no host code; every new
Protocol method has a default so later additions remain additive. The loader's
pre-1.0 rule requires an exact major.minor match, so extensions written against
0.1 are now refused at startup with an actionable install hint rather than
loading into a host that implements a different surface.
uv.lock records the contract package's new version, so `uv sync --locked` still
resolves on a fresh checkout.
* fix(backend): sort gateway service imports
* feat(extensions): observe task lifecycle and system model calls
PR 1 (#4636) gave extensions a middleware chain, and a middleware only sees
what passes through the agent graph. Two runtime surfaces stay invisible to
it: when a lead run or a subagent begins and ends, and the DeerFlow-owned
model calls made outside the graph. This slice adds both, with no new
Gateway surface -- routers, services, and the reference extension stay in
PR 3.
Contract (deerflow-extension-api 0.1.1)
---------------------------------------
Two contribution kinds join `middlewares` on the registry:
`task_lifecycle` (`on_task_start` / `on_task_stop`, receiving a `TaskInfo`
and a conservative `TaskOutcome` of completed / aborted / failed) and
`system_model_observer` (`on_system_model_call`, receiving a
`SystemOperationKind`, a `SystemModelRequest` snapshot, and a
`SystemModelResult` carrying either the response or the provider exception
plus a duration).
`SystemModelRequest.messages` normalizes to a tuple at construction. Goal
evaluation and memory extraction pass a message list while title generation
and summarization pass one prompt string, and a bare `str` already satisfies
`Sequence` -- without normalization an observer iterating `request.messages`
would silently walk characters. Copying also makes the frozen snapshot
immutable in fact rather than only by declaration, since observations may run
after the call site returns and keeps mutating its own list.
Registry marks and rollbacks become per-bucket and positional, so an
`install()` that fails after registering two different kinds cannot leave one
of them behind. `needs_task_store` now covers all three kinds: a deployment
that registers only lifecycle hooks still gets a task store.
Task lifecycle
--------------
The lead worker notifies start after the run has started and stop after
completion persistence and the completion hook, but before clearing the
finalizing barrier and publishing the stream end -- holding the barrier
across stop is what keeps a same-thread replacement run from overlapping this
task's lifecycle. Cancellation raised out of the stop notification is
deferred, not propagated in place, so a cancelled run still clears the
barrier and emits its end frame. A subagent with a parent `run_id` wraps its
execution in the same pair inside `finally`, reporting `parent_task_id` so a
delegation tree is reconstructable; a subagent without a `run_id` (embedded
client, standalone LangGraph Server) logs and skips rather than inventing a
parent. Contributors run in registration order inside one shared 3s budget
and every failure is logged and failed open.
System model calls
------------------
Four kinds cover the model calls the middleware chain cannot see: goal
evaluation, memory extraction, title generation, and summarization. Each site
reports both terminal paths without changing the provider exception the host
observes, short-circuits on `has_system_model_observers`, and passes the live
task store when the runtime has one (detached work gets an isolated store).
The sync summarization half stays unobserved on purpose -- it and its only
host caller are the sync side of an async-only runtime, so notifying there
would block a thread on a call site the host never reaches; the reason is
recorded at the call site.
The DeerMem backend must stay vendorable and cannot import the extension API,
so it reports through a new `MemoryCallbacks.on_memory_llm_result` host hook
that the DeerFlow-side callbacks translate into an observation.
Notification loop
-----------------
Extension resources must be touched on the loop that created them, but
subagents can execute on isolated loops and DeerMem runs on a worker thread.
The Gateway registers its serving loop before any runtime dependency starts
and resets it last through the exit stack, so every startup-failure and
cancellation path is covered. Awaited hooks raised on another loop are
dispatched across with `run_coroutine_threadsafe` and awaited under the same
budget; synchronous sites submit fire-and-forget work. Shutdown stops
accepting detached observations before the memory flush -- that flush runs on
a worker thread and can emit memory observations -- while keeping the loop
alive for awaited task hooks until run and subagent drain completes.
Tests
-----
`test_extension_task_lifecycle.py`, `test_extension_subagent_lifecycle.py`,
and `test_extension_system_model_calls.py` cover ordering, fail-open, budget
exhaustion, snapshot binding under a concurrent singleton replacement, the
loop-dispatch and shutdown-suspension paths, and both terminal paths at every
call site. `test_gateway_run_drain_shutdown.py` pins the stop-before-barrier
and drain ordering.
* fix(extensions): decide notification fail-open by origin, observe cancellation
`_notify_each` only guarded `Exception`, so a contributor letting a
`CancelledError` escape — an extension implementing an internal timeout with
cancellation, say — skipped its successors and reached the worker's
deferred-interrupt path, ending an otherwise successful run as cancelled.
Fail-open is about where a failure came from, not its base class: only a
genuine cancellation of the host task increments `Task.cancelling()`, so
propagate on that and contain everything else. `KeyboardInterrupt` /
`SystemExit` still propagate.
`observe_system_model_call` skipped observers on cancellation for the same
base-class reason, leaving goal / title / summarization silent on a terminal
path that is routine — interrupt/rollback admission and shutdown both cancel
the run task, with the provider tokens already spent. Awaiting observers there
is unreliable (a repeated cancel interrupts that await before any of them
runs), so report through the same non-blocking submission the synchronous
memory bridge uses, then propagate the cancellation untouched.
DeerMem keeps `BaseException` around its provider call, now with the reason
recorded: that path runs on a worker thread, where cancelling the awaiting
side never interrupts the running thread, so `CancelledError` cannot arrive
at all. Its host-hook wrapper narrows to `Exception` — only the hook's own
failures are non-fatal, and an observability path must not swallow a process
teardown signal.
* fix(extensions): warn on budget exhaustion, scope observer logs by task, propagate teardown
Review response on #4684:
- The memory observation bridge caught BaseException, which would swallow
a teardown signal raised while dispatching; it now catches Exception,
matching the boundary the DeerMem-side call site documents and tests.
- A notification-budget timeout raised mid-hook fell into the generic
hook-failure path and logged an asyncio-internal traceback; it now logs
a warning like the pre-hook budget skip, while a TimeoutError a
contributor raises on its own stays classified as a hook failure.
- System model observer logs passed the operation kind as the task id,
so log lines said "task goal/title/..."; they now carry the task
scope id alongside the kind.
* fix(summarization): own the run model for compaction; bound failure
With summarization.model_name: null the summary model resolved to
config.models[0] while the executing model is selected per run; when they
differ and models[0]'s provider is broken (expired key, quota, outage)
compaction silently failed every triggered turn and context grew unbounded
until the main provider 400s the run (#3103's shape), even though the run's
own model was healthy.
Model ownership is now sourced from the builders, not re-derived at runtime:
- The lead, subagent, and manual /compact builders each pass the resolved run
model into create_summarization_middleware(run_model_name=...). The middleware
no longer reads runtime.context / get_config(), which do not carry a custom
agent's or a subagent's resolved model, so a custom-agent lead run and a
distinct-model subagent now summarize with their own model, not models[0] /
the parent's. Runtime re-resolution and the per-name model cache are removed.
- model_name: null summarizes with the run's own model; an explicitly configured
summary model generates and falls back to the run model on failure. The
fallback is built lazily after the primary fails and its construction is
guarded, so a broken fallback cannot skip a healthy primary or escape the
automatic failure boundary.
Failure is bounded and side-effect-safe:
- An empty or whitespace-only response is treated as a generation failure, not a
valid summary, so compaction never removes all history for an empty replacement.
- compact_state/acompact_state take raise_on_failure independent of force: the
manual /compact path always surfaces a generation failure (even force=false)
and routes it to the existing ContextCompactionFailed path (HTTP 500 ->
frontend error toast) instead of an unconsumed response reason. The automatic
path leaves compaction state unchanged.
- before_summarization hooks fire only after a replacement summary exists.
SummarizationConfig.model_name, config.example.yaml, and docs/summarization.md
document the final lead/subagent/manual ownership rules.
Part of RFC #4346 (section A). Evaluating fraction/triggers against the run
model's profile (profile ownership) is a separate follow-up.
* fix(summarization): manual /compact model ownership + fail-open construct/parse
Manual /compact carried only agent_name, so it derived the run model from the
custom-agent model or config.models[0] and missed the request-selected model the
run path uses (request -> custom-agent -> default). Carry model_name through
ThreadCompactRequest and the frontend compact call, resolve with the same
precedence, and move the custom-agent config read off the event loop (asyncio
.to_thread) with user_id so the strict blocking-IO gate is not bypassed by the
broad except.
Make one summary attempt own its full lifecycle so the fail-open boundary covers
construction and response parsing, not just invocation: build each candidate model
lazily and guarded (a raising constructor falls through to the healthy run model
instead of breaking agent construction), build the model_name:null primary from the
run model rather than config.models[0], and run response text extraction inside the
invocation try so a failing .text accessor falls back instead of escaping compaction.
Adds factory-level constructor-failure, response-extraction-failure (sync/async), and
route-path model-ownership tests.
Phase 3 of #3875 — subagents previously inherited none of the lead's
context-compaction, so a deep-research subagent (max_turns up to 150)
could accumulate >1M cumulative input before max_turns/timeout/token_budget
engaged, even after Phase 2's budget capped the pathological tail.
- Gate the subagent runtime chain on the SAME ``app_config.summarization.enabled``
switch the lead reads (per maintainer guidance in #3875), via the shared
``create_summarization_middleware`` factory. One config covers both chains;
no separate ``subagents.summarization`` field. No-op when summarization is
off (factory returns None).
- ``skip_memory_flush=True`` on the subagent path: the factory otherwise
attaches ``memory_flush_hook`` (when memory.enabled), which flushes
pre-compaction messages into durable memory keyed by thread_id. Subagents
share the parent's thread_id, so without skipping the hook a subagent's
internal turns would pollute the PARENT thread's durable memory
(#3875 Phase 3 review point).
- Harden ``capture_new_step_messages`` to tolerate history contraction:
summarization rewrites the messages channel via
``RemoveMessage(id=REMOVE_ALL_MESSAGES)``, shrinking len(messages) below
the step-capture cursor. Without a reset, every step appended after the
compaction point was dropped until length overtook the stale cursor (#3845
interaction, maintainer validation point (a)). Cursor now resets to the
new tail; id/content dedup prevents re-emitting pre-compaction steps.
- Couple the DEFAULT token-budget ceiling to ``summarization.enabled``
(#3875 Phase 3 review point): 1M when compaction is on, 2M when off
(preserves Phase 2's deliberate headroom for summarization-off
deep-research runs that can exceed 1M). A user-set budget (global or
per-agent) always wins regardless of the switch. Flagged tunable.
The summarization middleware does not implement ``consume_stop_reason``, so
the Phase 2 guard-cap stop-reason channel is unaffected.
Refs: https://github.com/bytedance/deer-flow/issues/3875
- Add endswith('__user') guard in _is_user_injection_target to prevent
unbounded suffix growth (id__user__user__user...) when a prior ID-swap
peer is mistakenly treated as a new injection target
- Add peer rescue in _preserve_dynamic_context_reminders to keep
ID-swap __user and __memory messages out of summary compression,
preventing orphaned SystemMessage reminders and lost user context
- Add 6 regression tests covering both failure modes
* fix(summarization): tag summary LLM calls nostream to stop phantom stream messages (#2503)
The SummarizationMiddleware runs its summary LLM call inside a before_model
hook. Without a nostream tag the summary tokens were captured by LangGraph's
messages-tuple stream callback and broadcast to the frontend as a phantom AI
message.
Generate a dedicated summary model copy tagged with "nostream" (merged on top
of any existing tags such as "middleware:summarize" so RunJournal attribution
is preserved) and override _create_summary / _acreate_summary to invoke it
directly. This avoids temporarily swapping the shared self.model, which would
otherwise leak the RunnableBinding across concurrent runs and break parent
logic that inspects the raw model (profile / _get_ls_params).
Add regression tests covering nostream tagging, concurrent-run isolation, raw
model preservation, and existing-tag merge.
* fix(summarization): address nostream review feedback
* Refactor API fetch calls to use a unified fetch function; enhance chat history loading with new hooks and UI components
- Replaced `fetchWithAuth` with a generic `fetch` function across various API modules for consistency.
- Updated `useThreadStream` and `useThreadHistory` hooks to manage chat history loading, including loading states and pagination.
- Introduced `LoadMoreHistoryIndicator` component for better user experience when loading more chat history.
- Enhanced message handling in `MessageList` to accommodate new loading states and history management.
- Added support for run messages in the thread context, improving the overall message handling logic.
- Updated translations for loading indicators in English and Chinese.
* Fix test assertions for run ordering in RunManager tests
- Updated assertions in `test_list_by_thread` to reflect correct ordering of runs.
- Modified `test_list_by_thread_is_stable_when_timestamps_tie` to ensure stable ordering when timestamps are tied.
