* feat(checkpoint): make delta snapshot_frequency configurable
* fix(config): carry legacy checkpoint_delta_snapshot_frequency with warning
Addresses review on #4516: the rename from the flat
database.checkpoint_delta_snapshot_frequency key to nested
database.checkpoint_delta.snapshot_frequency silently dropped the old
value (pydantic extra="ignore"). Add a before-validator that maps the
legacy key onto the nested one with a deprecation warning (nested key
wins when both are set), plus a CHANGELOG breaking-change note covering
the rename and the 1000 -> 10 default change.
* fix(checkpoint): validate frozen snapshot frequency
* feat(checkpoint): production-shaped full/delta benchmark with configurable snapshot frequency
- Group benchmark scripts into per-family folders (checkpoint/, sandbox/)
- Extract shared benchmark infrastructure into checkpoint_bench_common.py
- Add checkpoint_delta_snapshot_frequency config (default 1000, process-frozen);
freeze it in make_lead_agent and DeerFlowClient; key the state-schema
adaptation cache by resolved frequency
- New bench_production.py: per-case child processes run N ainvoke turns through
the real lead-agent graph (scripted deterministic model, real AsyncSqliteSaver),
then measure GET /state + POST /history through the real Gateway route stack
in one event loop (httpx ASGITransport), cold/warm accessor-cache split,
cross-mode digest gates
- New summarize_production.py: delta/full ratios plus decision metrics
(snapshot_write_spike, cache_effect_ms, checkpoint_write_share,
auto-discovered history per-limit ratios)
* fix(checkpoint): address production benchmark review
* feat(browser): add agentic browser control
* fix(frontend): format browser view changes
* fix(browser): keep browser optional and isolate sidecar layout
* fix(browser): address PR review security and IME findings
- Nginx: add a browser-stream WebSocket location before the generic
/api/threads regex so Live upgrades instead of downgrading to HTTP
(both nginx.conf and nginx.local.conf).
- Ownership: require an existing owned thread for the WS stream and REST
navigate, and tear down the browser session on thread deletion so a
later caller cannot reuse a retained page/cookies by guessing the id.
- SSRF: enforce the URL policy at the browser request boundary via a
context-level route guard covering redirects, popups, iframes, and
subresources (skipped for CDP-attached Chrome).
- IME: skip key forwarding while a composition is active so confirming a
CJK candidate with Enter no longer submits the remote page form.
Adds regression tests for the request guard, session teardown on delete,
and the composing-Enter key decision.
* fix(frontend): smooth streaming in long tool threads
* Revert "fix(frontend): smooth streaming in long tool threads"
This reverts commit f0462516eabe77f138d4027ea1c714fb226683cf.
* fix(browser): address review security and lifecycle findings
- Reject cross-origin WebSocket upgrades on the live browser stream
(Origin allow-list reuse of CORS/same-origin helpers) to close a
WS-CSRF hole, and fail closed when the ownership store is absent.
- Warn when a CDP-attached session runs with the SSRF request guard
off, and drop the unreachable CDP screencast teardown dead code.
- Read browser session launch config from a single canonical source
(browser_navigate) so it is deterministic regardless of call order.
- Bound per-thread Chromium accumulation with idle-timeout eviction
and an LRU max-sessions cap.
- Reset the Live reconnect counter on a successful open so the stream
can't permanently stall after the cumulative attempt cap.
* fix(frontend): reduce long tool thread render stalls
Reuse stable historical message groups during streaming, defer heavy Markdown and browser previews, and lazy-decode message images.
* fix(browser): keep live control responsive during continuous input
Why: Manual browser control felt laggy — a physical click ran the remote
Playwright click three times and each non-move input synchronously awaited a
JPEG screenshot, so events queued behind capture (queue wait up to ~237ms).
The first async attempt used a trailing-edge debounce, which froze the visible
page until a wheel/keyboard gesture stopped ("scroll finishes, then it jumps").
What:
- Frontend forwards one `click` per physical click instead of also emitting
`down`/`up`, so the remote page is not clicked twice per gesture.
- Backend detaches live-frame capture from input dispatch: non-move actions
start a rate-limited background refresh loop (leading frame + bounded cadence)
that keeps emitting frames while input continues and never blocks dispatch.
- Add regression tests: input dispatch no longer awaits the screenshot, rapid
inputs coalesce, and continuous input keeps refreshing before it stops.
Scenarios: Verified in the live Browser panel — a single click completes in
~57ms (was blocked behind a 171ms capture), and a 1.14s sustained wheel gesture
renders ~7 frames throughout the scroll instead of one frame after it ends.
* fix(browser): harden worker and session lifecycle
* fix(browser): address latest review feedback
* fix(frontend): preserve optimistic new-chat message
* test(e2e): preserve mocked message run ids
* fix(browser): address capability review feedback
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
get_skills_prompt_section() without app_config resolved get_app_config()
only to read container_path, then let the enabled-skills load fall back
to the warm cache. On a cold start the cache is empty and the first call
returns an empty skills list while the synchronously-loaded disabled
section is populated, so manually assembled agents (create_deerflow_agent
style integrations) got a prompt with no enabled skills.
Rebind the resolved config so the storage and enabled-skills loads below
use it too; when no config is resolvable the cache-only fallback is
unchanged. Adds a cold-cache regression test.
Fixes#4144
Co-authored-by: fancyboi999 <fancyboi999@users.noreply.github.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(channels): add GitHub event-driven agents (#3754)
Add a webhook-driven GitHub channel with fail-closed webhook routing, deterministic per-agent PR/issue threads, mention-gated trigger fan-out, GitHub App token injection for sandboxed gh/git commands, and backend/AGENTS.md documentation.
* fix(llm-middleware): classify bare IndexError as transient
Upstream chat providers occasionally return 200 OK with an empty
generations list (observed against Volces "coding" on
ark.cn-beijing.volces.com). When that happens,
langchain_core.language_models.chat_models.ainvoke raises
``IndexError: list index out of range`` at
``llm_result.generations[0][0].message`` and kills the run.
Treat a bare IndexError reaching the middleware as a transient
upstream-payload glitch and route it through the existing
retry/backoff path instead of failing the whole agent run. The
retry budget and backoff schedule are unchanged.
Adds three regression tests covering the classifier and both the
recover-on-retry and exhausted-retries paths.
* fix(runtime): ignore stale LLM fallback markers from prior runs
When a run on a thread ends with the LLM-error-handling middleware emitting
a `deerflow_error_fallback`-marked AIMessage (e.g. after the IndexError
empty-generations classification fix lands), that message is persisted to
the thread's checkpoint as part of the messages channel. LangGraph replays
the full message history in `stream_mode="values"` chunks, so every
subsequent run on the same thread re-streams the stale fallback marker —
and the worker's chunk scanner faithfully picks it up, flipping
`RunStatus.success` to `RunStatus.error` for runs that themselves had
no LLM failure at all.
Snapshot the set of pre-existing message ids from the pre-run checkpoint
and thread it through `_extract_llm_error_fallback_message` /
`_try_extract_from_message` as a filter. Markers on history messages are
ignored; markers on fresh messages produced during this run still trip
the error path. Falls back to an empty set when the checkpointer is
absent or the snapshot can't be captured, preserving the prior behavior
on first-run / no-state paths.
Adds unit tests for the new filter (helper-level and `_collect_pre_existing_message_ids`)
plus an integration test exercising the full `run_agent` path with a stale
history checkpointer.
* fix(channels): make github channel fire-and-forget to avoid httpx.ReadTimeout on long runs
GitHub agent runs (clone -> edit -> test -> push -> PR) routinely exceed
the langgraph_sdk default 300s read deadline. The manager's runs.wait
call kept an HTTP stream open for the entire run lifetime, so the long
run blew up with httpx.ReadTimeout and the outer except branch then
released the dedupe key and emitted a false 'internal error' outbound.
The GitHub channel's outbound send is log-only by design: agents post to
the issue/PR via the gh CLI in the sandbox when they choose to comment
or create a PR. There is nothing for the manager to ferry back, so the
long-poll was pure overhead.
This change adds ChannelRunPolicy.fire_and_forget (default False) and
sets it True for the github channel. When fire_and_forget is True,
_handle_chat dispatches via client.runs.create (short POST, returns
once the run is pending) instead of client.runs.wait, and skips the
response-extraction + outbound-publish block. ConflictError on a busy
thread still trips the standard THREAD_BUSY_MESSAGE path so behavior on
the busy case is preserved for any future non-github fire-and-forget
channel.
Other (non-github) channels are unchanged: their policy defaults
fire_and_forget=False and they continue to dispatch via runs.wait.
Adds 6 regression tests in tests/test_channels.py::TestGithubFireAndForget:
- Default ChannelRunPolicy.fire_and_forget is False.
- The github policy registers fire_and_forget=True.
- github inbound calls runs.create, not runs.wait, with the right kwargs.
- github inbound publishes no outbound on success.
- ConflictError from runs.create still emits THREAD_BUSY_MESSAGE.
- Non-github channels (slack) still dispatch via runs.wait.
* test(lead-agent): accept user_id kwarg in skill-policy test stubs
The two GitHub-channel tests added in #3754 stubbed
_load_enabled_skills_for_tool_policy with a lambda that only accepted
`available_skills` and `app_config`, but the real function (and its call
site in agent.py) also passes `user_id`. This raised TypeError on every
run, failing backend-unit-tests.
Add `user_id=None` to match the three sibling stubs in the same file.
* refactor(gateway): disambiguate context-key set names
The two frozensets _INTERNAL_ONLY_CONTEXT_KEYS and _CONTEXT_ONLY_KEYS
shared a confusable "CONTEXT_ONLY" token in different orders, and the
first broke the _CONTEXT_<X>_KEYS pattern of its sibling
_CONTEXT_CONFIGURABLE_KEYS. Rename to make the distinct axes explicit:
_CONTEXT_INTERNAL_CALLER_KEYS - WHO: internal callers (scheduler) only
_CONTEXT_RUNTIME_ONLY_KEYS - WHERE: runtime context only, never configurable
Pure rename, no behavior change.
* 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>
`client.py` imported the private `_build_middlewares` from `agent.py` across a
module boundary and called it as public API. Because the `_` name signals
"module-private, no external callers", any future rename or signature change
silently breaks the embedded `DeerFlowClient` path — and the test suite even
monkeypatched `deerflow.client._build_middlewares`, baking the leak in.
`DeerFlowClient` is a lead-agent variant that genuinely needs the lead agent's
full middleware composition, so make the dependency honest: promote the helper
to a documented public entry point `build_middlewares` and update every in-repo
caller. Found during #3341 review; #3341 already removed one such leak
(`_assemble_deferred` -> public `assemble_deferred_tools`) and left this one out
of scope on purpose.
- agent.py: rename def + both internal call sites; expand the docstring into a
public-entry-point contract and document the previously-undocumented
model_name / app_config / deferred_setup params
- client.py: import + call site now use the public name (removes the last
cross-module private import)
- scripts/tool-error-degradation-detection.sh: update its import + call site
- tests (5 files): update monkeypatch/patch targets and direct calls
- docs (backend/CLAUDE.md, plan_mode_usage.md, middlewares.mdx): sync the live
references that describe the symbol as current API
Pure mechanical rename, no behavior change. Historical design docs (rfc,
superpowers spec) intentionally keep the old name as point-in-time records.
Closes#3431
* 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>
* feat(agent): 为AgentConfig添加skills字段并更新lead_agent系统提示
在AgentConfig中添加skills字段以支持配置agent可用技能
更新lead_agent的系统提示模板以包含可用技能信息
* fix: resolve agent skill configuration edge cases and add tests
* Update backend/packages/harness/deerflow/agents/lead_agent/prompt.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* refactor(agent): address PR review comments for skills configuration
- Add detailed docstring to `skills` field in `AgentConfig` to clarify the semantics of `None` vs `[]`.
- Add unit tests in `test_custom_agent.py` to verify `load_agent_config()` correctly parses omitted skills and explicit empty lists.
- Fix `test_make_lead_agent_empty_skills_passed_correctly` to include `agent_name` in the runtime config, ensuring it exercises the real code path.
* docs: 添加关于按代理过滤技能的配置说明
在配置示例文件和文档中添加说明，解释如何通过代理的config.yaml文件限制加载的技能
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
