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
---------
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.
---------
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
