* fix(subagents): align prompt and middleware subagent limit; allow min of 1
SubagentLimitMiddleware clamped max_concurrent to [2, 4] internally, but
agent.py and client.py fed the raw config value into the system prompt, so
a user-configured 1 (or 5) produced a prompt that disagreed with the
enforced middleware limit. Lower MIN_SUBAGENT_LIMIT to 1 and clamp the raw
config value with _clamp_subagent_limit() at both the agent factory and the
embedded client so the prompt and middleware see the same value.
* fix: remove unused imports MAX_CONCURRENT_SUBAGENT_CALLS, MIN_CONCURRENT_SUBAGENT_CALLS, clamp_subagent_concurrency
* fix: harmonize clamp range [1,4] across middleware, config, and prompt path; fix lint
- Changed MIN_CONCURRENT_SUBAGENT_CALLS from 2 to 1 so prompt.py's
clamp_subagent_concurrency and the middleware's _clamp_subagent_limit
both clamp to [1,4] — eliminating the divergence where the prompt told
the model 'max 2 task calls' but the middleware enforced 1.
- Applied _clamp_subagent_limit at build_middlewares (agent.py:360) so
all 3 construction sites (agent.py:360, agent.py:450, client.py:259)
consistently clamp the config-resolved limit.
- Derived MIN_SUBAGENT_LIMIT / MAX_SUBAGENT_LIMIT from
MIN_CONCURRENT_SUBAGENT_CALLS / MAX_CONCURRENT_SUBAGENT_CALLS so the
two module-level definitions stay in sync.
- Added TestConfigParity.test_prompt_path_and_middleware_clamp_agree
regression test.
- Fixed lint.
* fix(lint): add missing imports for MIN_CONCURRENT_SUBAGENT_CALLS and MAX_CONCURRENT_SUBAGENT_CALLS
* docs+test: update AGENTS.md clamp range to 1-4; add prompt/middleware parity regression test
- backend/AGENTS.md still documented the old [2,4] clamp in two places;
updated to [1,4] to match MIN_CONCURRENT_SUBAGENT_CALLS = 1.
- Added test_apply_prompt_template_single_subagent_limit_matches_middleware:
renders the real system prompt with max_concurrent_subagents=1 and asserts
the advertised HARD LIMITS value equals SubagentLimitMiddleware's enforced
max_concurrent — the end-to-end check that would have caught the [1,4] vs
[2,4] prompt-path divergence flagged in review.
* refactor: simplify per review — restore clamp delegation, drop redundant call-site clamps
Per willem-bd's review, reduce the PR to the one behavioral change plus
docs/tests:
- _clamp_subagent_limit delegates to clamp_subagent_concurrency again
instead of inlining a byte-identical copy; with a single source of
truth the TestConfigParity sync-check class is unnecessary — dropped.
- Revert the call-site clamps in agent.py (build_middlewares,
_make_lead_agent) and client.py (_ensure_agent) to main: both
downstream consumers (SubagentLimitMiddleware.__init__ and the prompt
path) already clamp internally, and the cross-module private import
of _clamp_subagent_limit goes away with them.
- Keep MIN_CONCURRENT_SUBAGENT_CALLS = 1 (the fix), the [1, 4]
docstring updates, the AGENTS.md range corrections, and the
end-to-end prompt/middleware parity test for single-subagent mode
(docstring reworded: on main a configured 1 was bumped to 2 by both
paths — there was no divergence to fix, just a silently raised floor).
* test: fix stale comment referencing reverted agent.py/client.py call-site clamps
---------
Co-authored-by: nankingjing <nankingjing@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(memory): pluggable + self-contained memory system (MemoryManager plan phases 1 & 2)
Phase 1 — Pluggable (steps 0-10):
- ABC MemoryManager (9 methods) + singleton factory + drop-in backend discovery
- DeerMem default backend with core/ (storage/queue/updater/prompt/message_processing)
- NoopMemoryManager backend (proves pluggability)
- All call sites (middleware/hook/prompt/gateway/client/app) routed through manager
- hasattr capability probing for DeerMem-internal methods (no hard imports)
- MemoryConfig gains manager_class field; shared vs DeerMem-private annotated
Phase 2 — Self-contained DeerMem (steps 11-18):
- backend_config passthrough + DeerMemConfig (all DeerMem-private fields moved off MemoryConfig)
- DI: DeerMem owns storage/queue/updater/llm as instance attributes (no global singletons)
- Storage independence: core/paths.py with own root (~/.deermem or ),
factory auto-injects deer-flow's runtime_home() as absolute base_dir (zero-config)
- LLM independence: core/llm.py via langchain init_chat_model (no create_chat_model)
- Trace independence: optional tracing_callback replaces inject_langfuse_metadata/request_trace_context
- Message processing independence: hide_from_ui default-skip + optional should_keep_hidden_message hook
- Internal imports → relative (only deer_mem.py ABC import is host-relative)
- Carrier (deer_mem.py adapter) / portable (deermem/ config+core) split
- New tests: test_deermem_self_contained + test_memory_manager_pluggable; all memory tests migrated
- Other-agent demo: samples/other_agent_demo/ + automated portability test
- config.example.yaml memory section updated to phase-2 schema
* feat(memory): port consolidation + staleness fix into self-contained DeerMem; phase-2 host hooks
Port upstream #3996 (memory consolidation) and #3993 (staleness KeyError fix)
from origin/MemoryManager into the pluggable, self-contained DeerMem structure
(backends/deermem/deermem/), adapted to the DI MemoryUpdater (config injected,
not get_memory_config globals):
- DeerMemConfig: add consolidation_enabled (opt-in, default false) /
consolidation_min_facts / consolidation_max_groups_per_cycle /
consolidation_max_sources
- prompt.py: factsToConsolidate JSON field + {consolidation_section} placeholder
+ CONSOLIDATION_PROMPT constant
- updater.py: _coerce_source_confidence / _select_consolidation_candidates /
_build_consolidation_section module helpers (matching the existing
_select_stale_candidates style); consolidation normalization in
_normalize_memory_update_data; consolidation apply in _apply_updates (after
max_facts trim, with apply-time guardrails mirroring staleness); staleness
KeyError fix (f["id"] -> f.get("id") is not None) applied to both the
staleness guardrail and the consolidation allowed_source_ids comprehension
- config.example.yaml: consolidation section under memory.backend_config
- tests/test_memory_consolidation.py: 40 DI-adapted tests (running, not skipped)
incl. the staleness KeyError regression
Also includes in-flight phase-2 host-integration work: storage_path semantics
(any absolute/relative value = root dir) and host-default tracing_callback /
should_keep_hidden_message hooks injected into backend_config by the factory.
Co-Authored-By: Claude <noreply@anthropic.com>
* feat(memory): add noop backend template and backends guide
- backends/noop/: complete drop-in template (config.py with zero deer-flow
imports, noop_manager.py with a 6-step new-backend walkthrough in its
docstring, commented optional fact-CRUD capabilities).
- backends/README.md: which files to touch when adding/swapping a backend,
the 5-item backend contract, and common pitfalls.
- manager.py: generalize backend examples in comments (drop mem0-specific
references).
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(frontend): guard formatTimeAgo against invalid timestamps
Return a neutral placeholder when the input date is invalid (e.g. an empty lastUpdated from a backend with no memories) instead of throwing 'Invalid time value' from date-fns.
Co-Authored-By: Claude <noreply@anthropic.com>
* feat(memory): wire tool-driven memory mode through the MemoryManager ABC
tools.py (memory_search/add/update/delete) now calls get_memory_manager()
instead of the removed host memory module, so tool mode (memory.mode: tool)
works for any backend. DeerMem.search is implemented (case-insensitive
substring match, ranked by confidence) as a stand-in for the planned
semantic retrieval; noop.search returns [] (unchanged). Fact-CRUD tools
use getattr+callable probing -- backends lacking those ops (noop) get a
clear JSON error instead of crashing.
Tests: test_memory_tools rewired to mock the manager (handler tests) +
TestModeGating retained; test_memory_search now covers DeerMem.search;
pluggable stubs test updated (search no longer a stub).
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: resolve lint errors (import sorting, type annotation quotes, E402 in skipped tests)
* docs: restore explanatory comments in config.example.yaml memory section
* fix(security): port html-escape memory facts fix (#4097) to vendored DeerMem prompt.py
* fix(memory): address review + port dropped upstream memory fixes
Review blockers (vendored DeerMem):
- #4044 restore _escape_memory_for_prompt (current_memory blob in
MEMORY_UPDATE_PROMPT) - prevents </current_memory> breakout
- #4028 html.escape staleness-section cat/content in _build_staleness_section
- #4119 add _escape_summary for injection-path summaries (Work/Personal/
Current Focus/Recent/Earlier/Background)
- default-model silent no-op: factory injects host default chat model via a
new host_llm slot (create_chat_model(name=None)); DeerMem prefers host_llm
over build_llm(model). Zero-config extraction works out of the box again
- MemoryConfigResponse: fix stale docstring (backend-agnostic shape; DeerMem
knobs live under backend_config, not top-level - restoring flat would
re-couple the API to DeerMem). Frontend audited: does not read /memory/config
- _host_default_tracing_callback: restore langfuse assistant_id/environment
- search: push category onto the ABC signature; DeerMem filters BEFORE the
top_k slice (was filtered client-side after slicing -> starved results)
- _do_update_memory_sync: split into wrapper+impl; bind trace_id into the
request-trace ContextVar on the Timer/executor worker via a new
trace_context_manager host hook (None trace_id left unbound - no fabrication)
- client.py fact-CRUD now passes user_id (was writing to the global bucket
while get_memory reads per-user)
- _resolve_manager_class: fail-fast (raise ValueError) on an unresolved
explicit manager_class instead of silently falling back to DeerMem (memory is
persistent state - a wrong store is a silent data-integrity footgun)
Upstream memory fixes dropped by the host->vendored rename conflict, re-ported
to backends/deermem/deermem/core/ (+ deer_mem.py):
- #4073 queue busy-timer-spin -> _reprocess_pending flag (core/queue.py)
- #4074 null source.confidence in staleness -> _coerce_source_confidence
(core/updater.py: _build_staleness_section + _apply_updates stale sort)
- #4075 factsToRemove is optional (drop from _REQUIRED_MEMORY_UPDATE_TOP_LEVEL_KEYS)
- #4076 null confidence in search ranking -> _coerce_source_confidence
(deer_mem.py DeerMem.search)
host_llm + trace_context_manager are host-injected via backend_config (factory
in manager.py), keeping backends/deermem/ at exactly one `from deerflow` line
(the ABC contract) - portability test preserved.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: resolve lint errors (F541 f-string without placeholders, E501 line too long)
* fix(memory): restore hide_from_ui clarification preservation, expose mode
Two memory-system fixes (F541/E501 lint was already fixed on this branch):
- filter_messages_for_memory: restore default preservation of well-formed
human_input_response clarification answers (v2 regression). The
self-containment refactor made the bare function skip ALL hide_from_ui when
no hook was passed, but upstream preserves well-formed clarification
responses by default (test_hide_from_ui_human_input_response_is_preserved).
Inline a host-agnostic _is_human_clarification_response mirror of
read_human_input_response as the default keep-decision; the host-injected
should_keep_hidden_message hook still overrides (production path unchanged).
Portable package stays zero `from deerflow`.
- /memory/config: expose `mode` (middleware|tool) in MemoryConfigResponse +
the config/status endpoints + client.get_memory_config. mode is a host-
shared, behavior-determining field missing from the response projection.
Sync tests (mock .mode; e2e assert mode present).
- Align manager_class field docstring with fail-fast behavior.
Tests: filter/self-contained/portability (35) + memory-config (4) pass;
ruff clean.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): resolve ruff format failures in memory module + tests
`make lint` runs `ruff format --check` in addition to `ruff check`; 8 memory
files had pending format changes -- 7 pre-existing (deer_mem, updater, tools,
test_memory_queue/router/search/tools) + message_processing from the
hide_from_ui fix. Apply `ruff format`: whitespace/wrapping only, no logic
change. 109 memory tests pass; ruff check + format --check both clean.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): address PR review - legacy field migration, fact_id contract, path/docs
Address willem-bd's review on PR head bc8bf0d4 (risk:high, persistent state):
- config: auto-migrate pre-abstraction top-level memory.* DeerMem fields
(storage_path, max_facts, debounce_seconds, model_name, token_counting,
staleness_*, consolidation_*) into backend_config on load + warn, so an
upgrade does NOT silently revert customized settings (was: silent
extra='ignore' drop). model_name -> backend_config.model.model. Unknown
top-level keys warned.
- factory: resolve a relative backend_config.storage_path against runtime_home()
(base_dir-relative, CWD-independent) to preserve pre-abstraction semantics;
paths.py stays portable (no runtime_home import).
- tools: memory_add uses the fact_id returned directly by create_fact instead of
re-deriving it via content-key matching (coupled the tool to the backend's
content normalization; could misreport a storage cap). create_fact now returns
(memory_data, fact_id); gateway/client/tool updated. Fix terse
{"error":"content"} -> {"error":"empty content"}.
- app.py: update stale token_counting=="char" warm-up comment to point at
manager.warm (DeerMem.warm re-checks char and returns early).
- router: comment explaining reload_memory silent fallback vs fact 501 asymmetry
(read-only degrade vs write fail-loud).
- CHANGELOG: document breaking changes (/memory/config + client.get_memory_config
shape flat->backend_config; custom storage_class path moved + __init__ must
accept config) and the legacy-field auto-migration.
- tests: add regression test pinning the per-user memory path
({storage_path}/users/{safe_user_id}/memory.json == host make_safe_user_id)
across the abstraction; update create_fact mocks for (memory_data, fact_id).
Tests: 273 passed (memory suite); ruff check + format clean.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): address PR review - storage_path, max_facts, tracing, parsing
Six review findings (willem-bd), each verified against upstream:
- storage_path semantics (file -> root dir): migration drops file-style
(.json) legacy values with a warning; factory raises if storage_path
resolves to an existing file (avoid silent NotADirectoryError write
failure). CHANGELOG + config.example.yaml comment updated.
- create_memory_fact enforces max_facts again (via _trim_facts_to_max) and
returns (memory, None) when the cap evicts the new fact; memory_add tool
reports "not stored", client raises ValueError, POST /memory/facts -> 409.
- max_facts trim uses _coerce_source_confidence (was raw f.get("confidence",
0) -> TypeError on non-float imported/legacy confidence, swallowed as
silent update failure).
- memory-tracing assistant_id restored to "memory_agent" (was "lead-agent"
copy-paste; matches upstream + DeerMem run_name).
- _is_human_clarification_response cross-checked against
read_human_input_response (drift guard test).
- empty-string legacy values skipped silently in migration (narrow fix, not
broad "if not value" which would skip explicit bool False).
8 new regression tests. make lint + 406 memory tests pass.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): address internal review - storage fail-fast, build_llm degrade, config warn, noop template
Addresses 4 findings from the PR #4122 internal supplemental review
(parallel to willem-bd's review, no overlap):
- create_storage fail-fast: a misspelled/unimportable storage_class now
raises ValueError instead of silently falling back to FileMemoryStorage.
Memory is persistent state, so a wrong store is a data-integrity footgun;
mirrors the existing manager_class resolution policy. (storage.py)
- noop template create_fact signature: the commented template used
keyword-only `content` and returned a bare dict, while DeerMem's actual
create_fact takes positional `content` and returns tuple[dict, str|None]
(the memory_add tool passes content positionally; gateway/client/tools all
tuple-unpack). A backend copied from the template would 500 on fact-CRUD.
Template fixed; delete_fact/update_fact templates left (callers compatible).
(noop_manager.py)
- build_llm graceful degrade: wrap init_chat_model in try/except, degrade to
None + WARNING on failure (mirroring _host_default_llm) so a misconfigured
explicit model does not crash app startup -- non-LLM memory ops still work
and an update raises at runtime with the error logged. (llm.py)
- from_backend_config unknown-key warning: log a WARNING for unknown
backend_config keys (mirrors the host layer's load_memory_config_from_dict)
so a typo like `storage_pat` does not silently fall back to the default and
write memory to an unintended location. (config.py)
Tests: rewrote 3 create_storage fallback tests to expect ValueError; added 4
tests (build_llm zero-config/degrade, from_backend_config warn/silent).
make lint green; full memory suite passes.
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: lllyfff <2281215061@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: lllyfff <122260771+lllyfff@users.noreply.github.com>
* fix subagent total delegation cap
* fix embedded subagent run cap context
* fix subagent cap config consistency
* fix resumed subagent run cap boundary
* fix legacy resume subagent boundary
* address subagent cap review feedback
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Replace the full-metadata <available_skills> system-prompt block with a
compact <skill_index> (names only) and an on-demand describe_skill tool
when skills.deferred_discovery: true (default: false / backward compat).
New modules:
- skills/catalog.py — SkillCatalog (immutable, searchable; select: has no
cap, keyword/prefix search caps at MAX_RESULTS=5)
- skills/describe.py — build_describe_skill_tool(catalog) closure;
build_skill_search_setup() wires SkillSearchSetup into both the
LangGraph agent factory (agent.py) and DeerFlowClient (client.py)
Changes:
- Skill @dataclass(frozen=True); allowed_tools/required_secrets list→tuple
- Skill First prompt line gated on skill_names (deferred vs legacy wording)
- get_skills_prompt_section: short-circuit storage on deferred path;
merge user_id (upstream) + skill_names (this PR) params
- describe_skill tool parameter named "name" (matches prompt wording)
- select: branch removes [:MAX_RESULTS] cap (exact request, not ranking)
- AGENTS.md: document deferred_discovery config field + new modules
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(skills): per-user skill isolation (#2905)
Implement user-scoped skill storage that isolates custom skills between
users while sharing public skills globally.
Key changes:
- Add UserScopedSkillStorage class for per-user custom skill directories
- Introduce get_or_new_user_skill_storage() factory with user_id context
- Auth middleware sets effective_user_id for request-scoped storage
- Agent/prompt/middleware now use user-scoped storage and prompt cache
- Sandbox mounts user-scoped skill directories for search/read tools
- Add validate_skill_file_path() to SkillStorage for path security
- Migration script supports --all-users bulk migration
- Frontend: add editable field to Skill type, error check in enableSkill
- All skill categories can be toggled (custom skills default to enabled)
- Update skill-creator SKILL.md with isolation-aware instructions
Tests:
- Add test_user_scoped_skill_storage.py (new)
- Update all existing skill tests for user-scoped storage
- Update sandbox, client, and router tests
* fix(skills): address second-round PR review feedback (#3889)
- P1-1: restrict legacy skill mount to users without custom skills
- P1-2: fail-closed for _is_disabled_skill_path (OSError → return True)
- P2-1: AND-merge global extensions_config skill disabled state
- P2-2: atomic write for _skill_states.json (mkstemp + replace)
- P2-3: normalize X-DeerFlow-Owner-User-Id in trusted boundary
- P2-4: LRU-bounded _enabled_skills_by_config_cache (OrderedDict, maxsize=256)
- P2-5: clear global prompt cache on PUBLIC skill toggle
- P2-6: invalidate skill caches on client.update_skill
* fix(tests): correct tool policy test after merge
* fix(skills): use DEFAULT_SKILLS_CONTAINER_PATH in UserScopedSkillStorage
The "/mnt/skills" literal in UserScopedSkillStorage.__init__ triggers
test_skill_container_path_defaults::test_mnt_skills_literal_is_owned_by_skill_constants_module
on CI. Migrate the default to the existing deerflow.constants constant,
matching the pattern already used by LocalSkillStorage, SkillStorage, and
the durable/tool_error middlewares.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(memory): add guaranteed injection for correction facts with graceful fallback
When the token budget is tight, high-value facts (e.g. user corrections)
can be silently evicted by lower-priority regular facts. This change:
- Introduces configurable 'guaranteed_categories' (default: [correction])
whose facts draw from a separate 'guaranteed_token_budget', ensuring
they are never dropped due to budget pressure.
- Adds a graceful fallback to confidence-only ranking when the
guaranteed-category path raises an unexpected exception.
- Refactors fact selection into a header-agnostic helper
(_select_fact_lines) with explicit token accounting in the caller,
eliminating double-counting of separators.
- Emits a single 'Facts:' header regardless of whether both guaranteed
and regular facts are present.
- Extends the final safety truncation limit to account for the
additional guaranteed budget so guaranteed facts survive end-to-end.
* refactor(memory): address review feedback on guaranteed injection
- Restore strict break-on-overflow in `_select_fact_lines` to preserve
the caller's confidence-ordered ranking; add a regression test locking
in the invariant that a shorter lower-confidence fact never slips
ahead of a skipped higher-confidence one.
- Account for the inter-group `\n` separator between guaranteed and
regular fact blocks in the regular budget (1-token precision fix).
- Clarify docstrings on `format_memory_for_injection` and
`MemoryConfig.guaranteed_token_budget` to distinguish the common
*displacement* case (total stays within `max_tokens`) from the rarer
*additive* case (safety-truncation ceiling raised when guaranteed
lines alone would overflow).
* fix(memory): address P1 safety truncation + P2s from review
- Structure-aware safety truncation: Facts block is now a protected
suffix so guaranteed-category facts can never be silently discarded
by a prefix-cut on overflow. Only the preceding (user/history)
sections are eligible for truncation.
- Extend the same protected-suffix treatment to the except/fallback
path by returning fact lines alongside the formatted section from
_fallback_format_facts, avoiding string parsing.
- Single inter-section separator: facts section no longer embeds its
own leading \n\n; the final "\n\n".join(sections) is the single
source of truth for section-to-section spacing.
- Bare string for guaranteed_categories now raises TypeError instead
of silently iterating single characters.
- Category-less / malformed facts no longer default-promote into the
guaranteed "context" pool — only facts with an explicit category
field qualify.
- Lift valid_facts pre-filter outside the try so the fallback path
reuses it instead of re-doing validation work.
- MemoryConfigResponse + DeerFlowClient.get_memory_config now expose
guaranteed_categories / guaranteed_token_budget.
- config.example.yaml: document the two new fields and bump
config_version from 12 to 13.
- Add regression tests for every finding.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(memory): add memory.token_counting config to avoid tiktoken network dependency (#3429)
Add a `memory.token_counting` option (`tiktoken` | `char`) so deployments in
network-restricted environments can opt out of tiktoken entirely. In `char`
mode the memory-injection budget uses a network-free character-based estimate
and never triggers the BPE download from openaipublic.blob.core.windows.net,
which could otherwise block for tens of minutes (see #3402).
Also harden the default `tiktoken` path:
- cache an in-flight LOADING sentinel so concurrent callers fall back
immediately instead of spawning more blocking get_encoding threads when the
first load is still running (e.g. under the 5s startup warm-up timeout);
- cache failures with a timestamp and retry after a cooldown so a transient
network outage self-heals back to accurate counting without a restart;
- skip startup warm-up entirely in char mode.
The new config is surfaced via the memory config API and config.example.yaml
(config_version bumped). Default remains `tiktoken`, so existing deployments
are unaffected.
* fix(memory): use CJK-aware char token estimate and address review feedback
- Replace the flat len(text)//4 fallback with a CJK-aware estimate so
Chinese/Japanese/Korean memory content does not over-fill the injection budget
- Document the internal tiktoken retry cooldown and char-mode escape hatch
- Sync CLAUDE.md / config.example.yaml / MEMORY_IMPROVEMENTS.md wording
- Fix MemoryConfigResponse mocks/assertions and add CJK estimate tests
* fix(#3189): prevent write_file streaming timeout on long reports
Adds a layered defense against StreamChunkTimeoutError caused by oversized
single-shot write_file tool calls:
- factory: default stream_chunk_timeout to 240s for OpenAI-compatible
clients (overridable via ModelConfig.stream_chunk_timeout in config.yaml)
- sandbox/tools: server-side 80 KB length guard on non-append write_file
calls (configurable via DEERFLOW_WRITE_FILE_MAX_BYTES env var, 0 disables);
rejects oversized payloads with a structured error pointing the model at
str_replace or append=True
- middleware: classify StreamChunkTimeoutError as transient but cap retries
at 1 via per-exception _RETRY_BUDGET_OVERRIDES (same-payload retry on a
chunk-gap timeout buffers the same way upstream; full 3-attempt loop
would stack 6-12 min of dead air)
- middleware: surface an actionable user-facing message for stream-drop
exceptions instead of leaking the raw langchain stack
- prompts: add a routing-style File Editing Workflow hint to both lead_agent
and general_purpose subagent prompts, pointing the model at str_replace
for incremental edits (mirrors Claude Code's Edit / Codex's apply_patch)
- tests: behavioural coverage for size guard, retry budget override,
stream-drop user message, factory default injection
Refs #3189
* fix(#3189): drop stream_chunk_timeout for non-OpenAI providers
Address CR feedback on PR #3195:
- factory: pop `stream_chunk_timeout` from kwargs for any model_use_path other than `langchain_openai:ChatOpenAI` instead of returning early. `ModelConfig.stream_chunk_timeout` is part of the shared schema, so a user-supplied value on a non-OpenAI provider would otherwise be forwarded to its constructor and raise `TypeError: unexpected keyword argument`.
- factory: rewrite docstring to describe the actual `exclude_none=True` behaviour (explicit null is excluded and falls back to the default) instead of the misleading "None falling out via exclude_none=True keeps its value".
- tests: add regression coverage asserting the kwarg is stripped before reaching a non-OpenAI provider's constructor.
Refs: bytedance#3189
* fix(#3189): restrict stream-drop user copy to StreamChunkTimeoutError only
Per CR on #3195: narrow _STREAM_DROP_EXCEPTIONS to StreamChunkTimeoutError. Generic httpx RemoteProtocolError / ReadError fall back to the standard 'temporarily unavailable' copy, since they routinely fire on transient network blips where the 'split the output' guidance is misleading. Retry/backoff classification is unchanged — both remain transient/retriable. Tests updated to reflect new copy, plus a symmetric regression test for ReadError.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(agent): add update_agent tool for in-chat custom-agent self-updates (#2616)
Custom agents had no built-in way to persist updates to their own SOUL.md /
config.yaml from a normal chat — `setup_agent` was only bound during the
bootstrap flow, so when the user asked the agent to refine its description
or personality, the agent would shell out via bash/write_file and the edits
landed in a temporary sandbox/tool workspace instead of
`{base_dir}/agents/{agent_name}/`.
Changes:
- New `update_agent` builtin tool with partial-update semantics (only the
fields you pass are written) and atomic temp-file + os.replace writes so
a failed update never corrupts existing SOUL.md / config.yaml.
- Lead agent now binds `update_agent` in the non-bootstrap path whenever
`agent_name` is set in the runtime context. Default agent (no
agent_name) and bootstrap flow are unchanged.
- New `<self_update>` system-prompt section is injected for custom agents,
instructing them to use `update_agent` — and explicitly NOT bash /
write_file — to persist self-updates.
- Tests: 11 new cases in `tests/test_update_agent_tool.py` covering
validation (missing/invalid agent_name, unknown agent, no fields),
partial updates (soul-only, description-only, skills=[] vs omitted),
no-op detection, atomic-write safety, and AgentConfig round-tripping;
plus 2 new cases in `tests/test_lead_agent_prompt.py` covering the
self-update prompt section.
- Docs: updated backend/CLAUDE.md builtin tools list and tools.mdx
(en/zh) with the new tool description.
* feat(agent): isolate custom agents per user
Store custom agent definitions under the effective user, keep legacy agents readable until migration, and cover API/tool/migration behavior with tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat: consistent write/delete targets & add --user-id to migration
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Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor: thread app config through lead prompt
* fix: honor explicit app config across runtime paths
* style: format subagent executor tests
* fix: thread resolved app config and guard subagents-only fallback
Address two PR review findings:
1. _create_summarization_middleware passed the original (possibly None)
app_config into create_chat_model, forcing the model factory back to
ambient get_app_config() and risking config drift between the
middleware's resolved view and the model's view. Pass the resolved
AppConfig instance through end-to-end.
2. get_available_subagent_names accepted Any-typed config and forwarded
it to is_host_bash_allowed, which reads ``.sandbox``. A
SubagentsAppConfig (also accepted upstream as a sum-type input) has
no ``.sandbox`` attribute and would be silently treated as "no
sandbox configured", incorrectly disabling the bash subagent. Guard
on hasattr and fall back to ambient lookup otherwise.
Adds regression tests for both paths.
* chore: simplify hasattr guard and tighten regression tests
- Collapse if/else into ternary in get_available_subagent_names; hasattr(None, ...) is False so the explicit None check was redundant.
- Drop comments that narrate the change rather than explain non-obvious WHY (test names already convey intent).
- Replace stringly-typed sentinel "no-arg" in regression test with direct args tuple comparison.
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Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
