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67 Commits
| Author | SHA1 | Message | Date | |
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PeaceMaker-best
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3c36217a51
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feat(observability): persist deferred tool promotions (#5183)
* feat(observability): persist deferred tool promotions Signed-off-by: PeaceMaker-best <221849497+PeaceMaker-best@users.noreply.github.com> * fix(ci): trim agent guidance chain Signed-off-by: PeaceMaker-best <221849497+PeaceMaker-best@users.noreply.github.com> --------- Signed-off-by: PeaceMaker-best <221849497+PeaceMaker-best@users.noreply.github.com> Co-authored-by: PeaceMaker-best <221849497+PeaceMaker-best@users.noreply.github.com> |
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Aari
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317577e285
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fix: enforce custom agent skill allowlists in sandboxes (#5077)
* fix: enforce agent skill allowlists in sandboxes * fix: guard E2B skill projection resets * fix: preserve agent skill isolation across delegation * fix: close sandbox skill isolation bypasses * fix(sandbox): close skill isolation review gaps * fix(sandbox): harden skill isolation lifecycle |
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Aari
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ff0a6768c2
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feat(subagents): add unified capacity and durable batch execution (#4998)
* feat(subagents): add capacity controls and durable batches * fix(helm): sync subagent config schema version * fix(subagents): preserve batch history without worker * fix(subagents): support explicit factory runtimes * fix: address durable batch review findings |
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Aari
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1aa813ddb3
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feat: add managed subagents and delegation scopes (#4887)
* feat: manage and scope subagents * fix: address subagent review feedback * fix: address managed subagent review feedback * fix: harden subagent settings semantics * fix: harden managed subagent cache invalidation * fix: reuse assembled lead agent inputs * fix: migrate managed subagent definitions --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Nan Gao
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13f0a7f263
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feat(extensions): let an out-of-tree extension observe what the agent did (#4863)
* 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
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KXH
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ae099c11ec
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fix(memory): scope bootstrap facts to custom agent (#4804)
Signed-off-by: KXH <shepherdlaurie238@gmail.com> |
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Nan Gao
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7389331e65
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feat(extensions): observe task lifecycle and system model calls (#4684)
* 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. |
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Nan Gao
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1f792d0f4b
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feat(extensions): add middleware plugin foundation (#4636)
* feat(extensions): add middleware plugin foundation * fix(extensions): stop config resolution from masking extension loading `create_app()` resolved the configured plugin list inside the fail-open guard around `load_extensions()`. CI has no `config.yaml` (gitignored and never generated by the workflow), so `get_app_config()` raised `FileNotFoundError` there and was swallowed as an extension failure -- `load_extensions()` never ran at all, and the four `create_app()` tests in `test_extension_app_loading.py` passed locally but failed on every runner. Resolve the plugin list before the guard. Only an absent `config.yaml` is tolerated, mirroring `_resolve_trace_enabled_for_app_construction()`: `create_app()` runs at import time, and lifespan still performs strict config loading before serving. A `config.yaml` that exists but fails to parse or validate now propagates instead of being reported as an extension failure -- reporting it as the latter silently dropped a `required: true` extension rather than failing the boot. Make the tests config-independent with an autouse `stub_app_config` fixture, following the existing pattern in `test_gateway_lifespan_shutdown.py`, and cover both new branches of the config-resolution boundary. * fix(extensions): bind the run's extension snapshot through subagent delegation The lead-agent path resolves one immutable loaded-extension snapshot per run and binds it through task-store allocation and graph construction, but the subagent path re-read the process-wide singleton at execution time. In production both are the same object, yet a `set_loaded_extensions()` between the lead run's start and a subagent's execution (test teardown, a future hot-reload path) would let one run mix two extension generations — exactly what the documented invariant exists to prevent. The graph-build binding is a ContextVar scoped to synchronous construction, so it has already exited by the time a tool delegates; the snapshot has to travel through runtime context instead. The run worker publishes it under the host-internal `EXTENSION_SNAPSHOT_CONTEXT_KEY` (written after the caller merge, popped when the run has none, so a caller-supplied value is never authoritative), `task_tool` reads it back through the type-checking `resolve_run_extensions()`, and `SubagentExecutor` binds it at construction. Callers outside the Gateway run path — embedded `DeerFlowClient`, standalone LangGraph Server — install no snapshot and keep the existing `get_loaded_extensions()` fallback. * refactor(extensions): defer the ordering table by call, not by a lying tuple `CORE_ORDERING_CONSTRAINTS` was a `tuple` subclass that overrode only `__iter__` and resolved into a class-level `_resolved` side channel. A tuple cannot populate its own storage after construction, so the instance stayed the empty tuple it was built as: `len()` was 0, `bool()` was False, `in` was always False, indexing raised, slicing and `reversed()` came back empty, and it compared unequal to the plain tuples tests substitute for it — all while iteration yielded the real constraints. Only `assert_ordering` consumed it, and only by iterating, so the split went unnoticed. The sibling `_AnchorTable(dict)` uses the same idea soundly because dict is mutable: `self.update()` fills the real storage, making every inherited operation correct. That trick does not survive the port to an immutable type. Replace it with `core_ordering_constraints()`, matching how `stack.py` defers the same kind of table via `_anchors()`. The deferral is kept — it is about dependency direction, not just cycles: `extensions/` is the layer the middleware layer calls into, so a module-scope `agents.middlewares` import here points the dependency backwards and closes a cycle as soon as any middleware imports something under `extensions/` at module level. Resolution stays at `assert_ordering` time, which already runs inside the middleware builder. Tests pin both halves: the returned value is a plain tuple whose len/bool/ membership/indexing/reversal/equality agree with iteration, and a subprocess probe asserts importing `extensions.ordering` does not load the middleware layer while calling the function does. |
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hataa
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540940bac1
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feat(authz): enforce model authorization at Gateway routes and runtime (#4063 Phase 3) (#4540)
* feat(authz): enforce model authorization at Gateway routes and runtime (#4063 Phase 3) Phase 3 / Models — the first of three resource-type PRs (Models, Skills, Sandbox). The RBAC provider already maps "model" → config key "models" (rbac.py _RESOURCE_POLICY_KEYS), so no schema change is needed. Gateway route layer (mirrors Phase 2A): - resolve_model_authorization() in authz.py returns (provider, principal), reusing _get_cached_route_provider and build_principal_from_context, including the INTERNAL_SYSTEM_ROLE → None pop for internal callers. - list_models filters via provider.filter_resources(principal, "model", names). - get_model checks provider.authorize("model", "use"). Deny → 403 (not 404, since the model exists but the role lacks permission). Runtime resolution layer (mirrors Phase 1B): - _authorize_model_name() in agent.py runs after _resolve_model_name. On deny, falls back to the first allowed model (RFC §9: graceful, not crash). All models denied + fail_closed → ValueError (matches existing contract). authorization.enabled: false is a complete no-op on both layers. Anonymous requests (user=None) bypass filtering. 18 new tests + 314 existing tests pass. * fix(authz): enforce model:use on the embedded DeerFlowClient path (Phase 3 follow-up) Round 4 review (willem-bd): _authorize_model_name only covered the Gateway runtime path (_make_lead_agent). The parallel lead-agent construction path DeerFlowClient._ensure_agent (client.py) filtered tools but not the model, so a library/embedded consumer with role-scoped model policies could run a model the role is denied model:use for. - Insert _authorize_model_name in _ensure_agent, mirroring _make_lead_agent. - Resolve None default to the first configured model before the gate so the implicit default (create_chat_model(name=None)) is also authorized. - Update test_authorization_filters_framework_tools_and_reuses_provider: the stub provider now returns an allow decision for model:use (checked during assembly) and patches resolve_authorization_provider in the agent namespace. - Add 3 DeerFlowClient._ensure_agent path tests (real-path fallback, None-default resolution, disabled no-op); 24 tests total. * docs(authz): document get_model provider-unavailable fail-open path + test zhfeng review (round 5): get_model's docstring only mentioned the deny→403 path, not the provider-resolution-error + fail-open path (which allows the request, mirroring list_models's documented fail-open semantics). The behavior itself is correct and symmetric with list_models, but it was undocumented and the _AuthorizationUnavailable path had no test coverage. - Extend get_model docstring to state the provider-error fail-closed/fail-open outcome, matching list_models's wording. - Add test_get_model_provider_unavailable_fail_closed_vs_open exercising the _AuthorizationUnavailable path (provider cannot be resolved at all), pinning fail-closed→403 / fail-open→200. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Vanzeren
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352f247a81
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feat(memory): add mem0 HTTP memory backend (#4528)
* feat(memory): add mem0 HTTP memory backend * fix(memory): address mem0 review feedback --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Vanzeren
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c48de5e70b
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feat(checkpoint): make delta snapshot_frequency configurable (#4516)
* 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 |
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阿泽
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ea74367502
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fix(runtime): honor LangGraph Server identity for user-scoped data (#4538)
* fix(runtime): honor LangGraph Server identity for user-scoped data * fix(runtime): scope custom agent SOUL by resolved user |
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Vanzeren
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e01173d8b2
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bench(checkpoint): production-shaped full/delta benchmark with configurable snapshot frequency (#4467)
* 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 |
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Zhengcy05
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2f60bee388
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fix: surface length-capped model responses (#4309)
* fix: surface length-capped model responses * fix: avoid the influence of the mid-turn * fix: correcting semantic annotations * fix: add ModelLengthTerminationDetector to compatible providers * fix:delete redundancy code * fix:supplementing log information improves observability * fix: align the document and complete the assertions. * fix: unit test * fix: revert AGENTS.md * fix: unit test * fix: add annotation and skip AIMessage has empty content |
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Aari
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37c343fe30
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fix(summarization): summarize with the run model, fall back on summary-provider failure (#4361)
* 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. |
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hataa
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7857fa0cce
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feat(authz): enforce tool authorization at assembly and runtime (#4370)
* feat(authz): enforce tool authorization at assembly and runtime * fix(middleware): guard deferred tool setup lookup (#4370) --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Ryker_Feng
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20debf9cc7
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feat(agents): per-agent model and generation settings (#4347)
* feat(agents): per-agent model and generation settings Let each custom agent choose its own model and sampling settings (temperature, max_tokens) plus thinking / reasoning_effort defaults, so agents sharing a model profile are no longer stuck with one shared temperature and output length (#4336). AgentConfig gains optional model_settings / thinking_enabled / reasoning_effort (None = inherit). create_chat_model applies per-caller model_overrides on top of the profile before the thinking/Codex transforms; the lead agent resolves each knob with precedence request > agent config > profile/default. The /api/agents create/update routes persist the fields and reject an unknown model. The default lead agent path is unchanged (no agent config -> overrides None). The agent chat composer also stops force-overriding an agent's configured default model with models[0]. * fix(agents): tri-state thinking control and default-model capability gating The model-settings dialog seeded the thinking switch to false, so opening it to tweak temperature and saving silently disabled thinking (the runtime default is on) with no way back to inherit. It also hid the thinking / reasoning controls whenever the agent inherited the global default model, since `__default__` never resolved through `models.find`. Give thinking an explicit Inherit / On / Off tri-state so an untouched save is a no-op, and resolve `__default__` to the effective default (models[0]) for the capability check. Logic lives in the tested helpers module. |
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Vanzeren
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42baed8c8c
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feat(checkpoint): dual-mode checkpoint storage with LangGraph DeltaChannel (#4292)
* feat(checkpoint): dual-mode checkpoint storage with LangGraph DeltaChannel
Add a restart-required database.checkpoint_channel_mode ("full" default,
"delta") that stores the messages channel via LangGraph 1.2 DeltaChannel,
cutting checkpoint storage from O(n^2) to O(n) for append-only history.
Existing full checkpoints seed delta state transparently; no data migration.
- config: mode schema + freeze-on-first-use with
CheckpointModeReconfigurationError; mode marker persisted in checkpoint
metadata; unsafe delta->full downgrade rejected fail-closed with
CheckpointModeMismatchError (run-level error, failed state read)
- state: delta message state schema; CheckpointStateAccessor centralizes
materialized reads for all consumers (threads API, branches,
regeneration, compaction, state updates, memory, goal workers)
- runtime: raw writers (run durations, interrupted title, thread goal)
parent their checkpoints to the checkpoint they derive from, preserving
delta ancestry; rollback forks the pre-run lineage through a state
mutation graph with Overwrite restores; InMemorySaver delta-history
override delegates to the base walk (fixes dropped first write after
migration, also present upstream)
- tests: conformance suite over {memory, sqlite, postgres} covering
migration replay, stable message IDs, storage shape and writer
preservation; conftest fixture isolates the frozen mode between tests;
stale config fakes refreshed
- ci: backend unit tests gain a postgres service
* fix(checkpoint): close materialization gaps in goal flow, guard public factory
- Route goal-continuation message reads through CheckpointStateAccessor:
raw channel_values reads see the delta sentinel in delta mode, which
disabled goal continuation (stand_down=no_durable_end_of_turn) after
durable assistant turns. Raw tuples remain for tuple-only metadata
(checkpoint id, pending_writes).
- Reject checkpoint_channel_mode='delta' + checkpointer in
create_deerflow_agent at construction: factory-built persisted graphs
bypass mode-marker injection and the fail-closed gate, reproducing
silent mixed-mode state loss. Delta without persistence stays allowed.
- Import the postgres saver lazily (pytest.importorskip in the fixture)
so the documented default install collects the suite; add a CI job
running pytest --collect-only on uv sync --group dev without extras.
- Fix test_checkpointer fallback test to patch get_app_config at its
use site (provider module), making it deterministic when a local
config.yaml selects a persistent backend.
* fix(gateway): preserve extension-owned channels in state mutations, bump config version
- build_state_mutation_graph / build_checkpoint_state_mutation_accessor
accept an explicit state_schema; branch and POST /state now compile the
mutation graph from the thread's effective schema
(graph_state_schema on the assistant graph). The base-ThreadState
fallback silently discarded channels contributed by custom
AgentMiddleware.state_schema on branch (data loss) and returned a
false-success 200 on POST /state.
- POST /state validates values keys against the mutation graph's
channels and rejects unknown fields with 422 instead of ignoring
them; reducer detection covers extension channels
(BinaryOperatorAggregate or DeltaChannel) so Overwrite replace
semantics work for middleware reducers in both modes.
- Endpoint regression: custom AgentMiddleware.state_schema value
survives branch, updates through POST /state, and an unknown field
receives 422.
- config_version 26 -> 27 for the new database.checkpoint_channel_mode
(example, Helm chart values + README, support-bundle fixture), so
existing installs get the outdated-config warning and
make config-upgrade merges the field; covered by a test driving the
real example file and the real config-upgrade script.
* fix(gateway): resolve assistant schema via one boundary, copy branch reducer values with Overwrite
GET /threads/{id}/state now resolves the thread's assistant_id through a
single reusable boundary (thread metadata -> assistant_id -> effective
graph), so channels contributed by a custom AgentMiddleware.state_schema
are materialized instead of dropped by the default lead schema. POST
/state uses the same boundary instead of resolving the schema ad hoc.
Branch writes wrap every copied reducer channel in Overwrite (derived
from the effective mutation graph: BinaryOperatorAggregate + DeltaChannel),
not just messages, so already-aggregated values are never re-merged.
Regression tests use a real AgentMiddleware.state_schema with a
non-identity reducer in both full and delta modes: GET /state returns the
extension value, POST /state replaces it, branch preserves it
byte-for-byte; the unknown-field 422 is a separate assertion.
* refactor(checkpoint): collapse read-path round-trips and ship dual-mode parity tests
Address review round 4 on PR #4292:
- Push ahistory/history limit through Pregel into checkpointer.alist
(SQL LIMIT) instead of materializing all rows and breaking in Python
- Fold the read-side mode-compat gate onto the returned snapshot's
metadata; only writes keep the pre-write tuple fetch (fail-closed)
- Cache factory-built accessor graphs per (assistant_id, mode) with
factory-identity revalidation; state reads no longer build a lead
agent per request
- get_thread: one snapshot fetch + one raw pending_writes fetch on the
resolved checkpoint (post-checkpoint __error__ writes never surface
in snapshot.tasks; verified empirically)
- DeerFlowClient.get_thread: single checkpointer.list walk collects
pending_writes per checkpoint instead of N get_tuple calls
- InMemorySaver delta-history patch: stand-down when the upstream
override disappears, try/except guard, validated-version warning,
guard tests
- make_lead_agent mode precedence: first freeze is owned by app_config
(client-supplied configurable key ignored); once frozen, injected
key/app_config must match or fail closed
- Rollback: lock in non-message channel restoration via fork
inheritance with a dedicated reducer-channel test
- Add tests/test_threads_checkpoint_mode.py and
tests/test_gateway_checkpoint_mode.py referenced by AGENTS.md and
the PR validation section: lifecycle parity (memory + sqlite),
per-step blob-count storage guard, gateway endpoint parity
Counted-saver tests pin checkpoint round-trips for aget/ahistory so
these regressions cannot silently return.
* fix(checkpoint): precise mode-mismatch HTTP mapping, gate E2E, and accessor resilience
- threads router: map CheckpointModeMismatchError to 409 (with cause and
thread id) and CheckpointModeReconfigurationError to 503 across all state
endpoints instead of swallowing both into a generic 500
- gate coverage: seed a real delta checkpoint into AsyncSqliteSaver and
assert aget/aupdate/ahistory fail closed; assert 409 at the HTTP boundary
through the real route stack
- rollback: compile the restore mutation graph with the thread's effective
state schema per the build_state_mutation_graph contract
- inheritance contract locks: rollback and manual compaction preserve
middleware-contributed channels via checkpoint fork cloning
- services: revalidate the accessor-graph cache against app_config identity
so config.yaml hot-reloads never serve a stale compiled graph
- services: degrade full-mode state reads to raw checkpointer reads when the
agent factory is unavailable (delta gate still applies; delta mode has no
fallback)
- deps: override websockets==16.0 (langgraph-sdk 0.4.2's <16 pin silently
downgraded 16.0 -> 15.0.1; pin is not grounded in any API incompatibility)
and bump the langchain lower bound to what the lockfile actually resolves
* fix(checkpoint): include anchor checkpoint in degraded history walk + cover get_thread
- _RawCheckpointReadAccessor.ahistory: alist(before=...) is exclusive while
pregel's get_state_history treats config.checkpoint_id as the inclusive
start; fetch the anchor explicitly so both read paths paginate identically
- extend the degraded-path gateway test: GET /thread returns raw values, and
POST /history with before starts at the anchor checkpoint
* fix(gateway): preserve degraded checkpoint timestamps
* fix(gateway): harden degraded checkpoint access
* fix(gateway): resolve assistants for checkpoint reads
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
|
||
|
ChenglongZ
|
ca16b64b26
|
feat(agent): support config-declared lead middlewares (#3964)
* feat(agent): support config-declared lead middlewares * fix(agent): preserve configured extension middlewares * fix(agent): address middleware extension review * fix(agent): tighten middleware extension docs and tests |
||
|
Aari
|
cd34a1a504
|
fix(skills): don't attach model tracing to the in-graph skill security scan (#4252)
* fix(skills): don't attach model tracing to the in-graph skill security scan * fix(skills): pass attach_tracing explicitly from the in-graph scan call site Follow the tracing INVARIANT's own convention rather than detecting the call context: scan_skill_content takes an attach_tracing flag, and _scan_or_raise -- the single in-graph choke point -- passes False. Standalone callers (Gateway skill routes, installer) keep the default True. The INVARIANT list named four sites and asks that new in-graph calls be added to it; record this fifth one so a future audit of that list finds it. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
Huixin615
|
f9340c1f08
|
test(mcp): cover passive skill tool visibility (#4247)
* test(mcp): cover passive skill tool visibility * test(mcp): tighten deferred discovery coverage |
||
|
Huixin615
|
65afc9b1d2
|
fix(skills): apply allowed-tools only to active skills (#4098)
* fix(skills): scope allowed-tools to active skills * fix(skills): tolerate stale active skill paths * chore: retrigger CI * fix(skills): document policy activation limits * perf(skills): reuse per-step tool policy decisions * fix(skills): harden runtime tool policy contracts * fix(skills): redact cached policy decisions * fix(skills): make slash tool policy authoritative * fix(skills): preserve policy-safe discovery tools * test(skills): cover explicit task delegation policy |
||
|
DanielWalnut
|
4e209827f3
|
feat(agent): Add subagent total delegation cap (#4115)
* 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> |
||
|
Ryker_Feng
|
41658c5ff4
|
feat(skills): add skill review quality gate (#4037)
* feat(skills): add skill review quality gate * fix(skills): skip review eval fixtures in CI * fix(skills): ignore review eval fixtures in bundled scans * fix(skill-review): harden review gate boundaries * fix(skills): address skill review gate feedback |
||
|
Huixin615
|
6389fc03fd
|
fix: ensure visible response after tool runs (#4033)
* fix: ensure visible response after tool runs * fix: clean up terminal response recovery state * fix: bound terminal recovery state |
||
|
Vanzeren
|
c2002d9fac
|
feat(memory): add memory tool sets (#4023)
* feat: add memory-as-tool mode alongside existing middleware mode - Add memory.mode config field (middleware|tool, default middleware) - Add search_memory_facts() for case-insensitive fact lookup - Add 4 memory tools: memory_search, memory_add, memory_update, memory_delete - Wire mode gating in factory.py and lead_agent/agent.py - 256 memory tests passing, zero regressions * fix: harden tool-mode memory scoping and docs * fix: address memory tool mode review feedback * fix(memory): address tool mode review feedback * fix: update config_version to 22 in values.yaml |
||
|
Ryker_Feng
|
ebc09ce130
|
feat(mcp): auto-promote deferred MCP tools from routing hints (#4019)
* feat(mcp): auto-promote deferred MCP tools from routing hints
When tool_search.enabled=true defers MCP tool schemas, PR1 routing hints
still require the model to spend a tool_search discovery round trip before
it can call the tool the routing metadata already points at. This adds a
McpRoutingMiddleware that matches the latest user message against PR1
routing keywords and promotes the matching deferred schemas before the
model call, removing that round trip.
Design (soft routing, opt-in, additive):
- Matches only the latest real HumanMessage (shared is_real_user_message
helper, reused by SkillActivationMiddleware so the two cannot drift);
case-insensitive substring match, no tokenizer dependency.
- Ordering: priority desc, then tool name asc; capped by the new global
tool_search.auto_promote_top_k (default 3, clamped 1..5). Does not add or
consume a per-tool auto_promote_top_k (PR1 schema unchanged); a per-tool
value is ignored with a DEBUG note.
- Returns a plain {"promoted": ...} state update (not a Command) and relies
on ThreadState.merge_promoted for union/dedupe, so auto-promote and a
model-triggered tool_search converge on the same catalog hash.
- Installed before DeferredToolFilterMiddleware on every deferred-tool path
(lead agent, subagent, embedded client, webhook via shared builders);
a construction-time assert rejects the reversed order. catalog_hash is
None / no routing index is a complete no-op, so bootstrap and ACP skip it.
- Privacy: never executes tools, never promotes policy-filtered tools, adds
no routing keywords or matched tool names to trace metadata or INFO/WARN
logs.
No behavior change when tool_search.enabled=false.
Tests: index construction, matching semantics, middleware state updates,
same-cycle deferred-filter interaction, lead/subagent/embedded-client
builder wiring + order invariant, config clamping, config.example.yaml
parseability, and privacy assertions.
* refactor(mcp): address auto-promote review nits
- executor: access app_config.tool_search.auto_promote_top_k directly to match
the lead-agent and embedded-client paths (drop the over-defensive getattr that
masked missing config); update the subagent test mock to carry tool_search.
- tool_search / mcp_routing_middleware: cross-reference the duplicated routing
priority/keyword normalization between the builder and the middleware's
defensive _normalize_index so they cannot silently drift.
- MCP_SERVER.md: document that auto-promote keyword matching is a case-insensitive
substring test (not word-boundary), advising distinctive keywords.
|
||
|
Ryker_Feng
|
5ba25b06ec
|
feat(mcp): add MCP routing hints (#4004)
* feat: add MCP routing hints * test: isolate mcp routing prompt config * fix: address mcp routing review feedback |
||
|
Ryker_Feng
|
26d7a5970d
|
feat: add manual context compaction (#3969)
* feat: add manual context compaction * fix: harden manual context compaction |
||
|
Tianye Song
|
15454b6fec
|
feat(skills): deferred skill discovery via describe_skill tool (#3775)
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> |
||
|
Zheng Feng
|
dcb2e687d5
|
feat(channels): add GitHub as a webhook-driven channel (#3754)
* 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. |
||
|
Xinmin Zeng
|
4fc08b4f15
|
feat: add scheduled tasks MVP (#3898)
* feat: add scheduled tasks MVP
* fix: harden scheduled task execution semantics
* feat(scheduled-tasks): preset-driven schedule form with timezone and live preview
Replace the raw cron input with a preset Select (hourly/daily/weekly/monthly/custom)
plus structured inputs (time picker, weekday toggles, day-of-month), datetime-local
for one-time tasks, a timezone selector defaulting to the browser timezone, and a
live human-readable preview. Reuses one ScheduledTaskScheduleInput for create and
edit; backend contract unchanged; zero new deps (pure Intl + DST-safe offset helpers).
* feat(scheduled-tasks): full-page i18n + recipe templates + E2E locale pin
Localize the rest of the scheduled-tasks page (filters, detail pane, actions,
edit form, run list, enum values) via t.scheduledTasks.* in en/zh. Add four
built-in recipe templates (GitHub Trending, news digest, issue triage, weekly
report) exposed as a chip row that pre-fills title + prompt + schedule. Pin
Playwright locale to en-US so E2E selectors stay stable against i18n. No backend
change, no new deps.
* fix(scheduled-tasks): idempotent 0003 migration, update head constants, future-date once test
Merge with main surfaced three CI failures:
- 0003_scheduled_tasks create_table collided with legacy test seeds that
build from full metadata; guard with inspector.has_table so the revision
no-ops when the table already exists (0004/0005 are already idempotent via
_helpers.py).
- persistence bootstrap concurrency/regression tests pinned HEAD to main's
0002_runs_token_usage; bump to the new head 0005_scheduled_task_thread_nullable.
- once-task router test used a fixed past run_at and tripped the
must-be-in-the-future validation; use a future date.
* address review: ok-check, 502 for trigger failure, mock fields, migration filename, doc fences
- fetchThreadScheduledTasks now checks response.ok like the other fetchers.
- trigger endpoint returns 502 (not 409) when dispatch fails outright, so
clients can distinguish a real conflict from a server-side failure.
- E2E mock normalizes scheduled-task objects with context_mode/last_thread_id
and nullable thread_id, matching the backend contract the UI renders against.
- Rename 0002_scheduled_tasks.py -> 0003_scheduled_tasks.py to match its
revision id (file was renamed in spirit already; filename now follows).
- CONFIGURATION.md: close the Tool Groups yaml fence and drop the stray fence
after the Scheduler notes so the sections render correctly.
* fix(scheduled-tasks): harden lease, poller, config, and frontend UX after review
* fix(scheduled-tasks): harden run lifecycle, overlap skip, non_interactive gating, and DST conversion after review
- defer a once task's terminal status to the run-completion hook; the task
stays running until the real outcome, and a startup sweep cancels once
tasks orphaned by a crash (launch-time 'completed' could stick forever)
- record interrupted runs as a distinct 'interrupted' run status with a
readable message; an interrupted once task ends 'cancelled', not 'failed'
- enforce overlap_policy=skip for fresh_thread_per_run via an active-run
pre-check (same-thread ConflictError can never fire across fresh threads)
- protect terminal run statuses from the late launch-path 'running' write
- honor context.non_interactive only for internally-authenticated callers;
arbitrary clients can no longer strip ask_clarification
- fix DST-stale timezone offset in zonedLocalToUtcIso by re-deriving the
offset at the resolved instant (once tasks fired an hour late around
spring-forward and the create->edit round-trip diverged)
- drop dead ScheduledTaskRunRepository.update_by_run_id; share one Gateway
API error helper between channels and scheduled-tasks frontends
* fix(scheduled-tasks): close review round-3 gaps in guards, concurrency, and API ergonomics
- scrub internal-only context keys (non_interactive) from the assembled run
config for non-internal callers: gating body.context alone left the same
key smuggle-able through the free-form body.config copied verbatim by
build_run_config
- guard update_after_launch with protect_terminal so the launch bookkeeping
write cannot clobber a once task already finalized by a fast-failing run's
completion hook (parent-row sibling of the run-row guard)
- reject a manual trigger while the task has an active run (409) instead of
launching a duplicate concurrent run on fresh_thread_per_run
- re-arm a terminal once task to enabled when PATCH pushes run_at into the
future; previously the endpoint returned 200 with a next_run_at that could
never be claimed
- make max_concurrent_runs a real global cap: each poll claims only into the
remaining budget of active (queued/running) scheduled runs
- paginate GET /scheduled-tasks/{id}/runs (limit<=200, offset) and push the
thread filter of /threads/{id}/scheduled-tasks into SQL
- stamp context.user_id on scheduler-launched runs, matching IM channels, so
user-scoped guardrail providers see the owning user
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
|
||
|
Zhipeng Zheng
|
53a80d3ad1
|
feat(skills): per-user custom skill isolation with sandbox mounting (#3889)
* 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> |
||
|
kongxiangxin
|
9d7e131340
|
fix(skills): preserve read_file for lead skill loading (#3862) (#3863) | ||
|
AochenShen99
|
442248dd06
|
feat: preserve durable context across summarization (#3887)
* feat: preserve durable context across summarization * fix: harden durable context review gaps * style: format delegation ledger live test * chore: remove stale delegation ledger prefix * fix: address durable context review feedback |
||
|
Nan Gao
|
af0d14d296
|
feat(subagents): system-maintained delegation ledger to stop redundant re-delegation (#3877)
* feat(subagents): add delegations ledger field + reducer to ThreadState * feat(subagents): pure helpers to derive + format the delegation ledger * feat(subagents): DelegationLedgerMiddleware records + injects the ledger * feat(subagents): register DelegationLedgerMiddleware for lead when subagents enabled + docs * add runtime log * chore(subagents): make delegation-ledger injection log production-ready * test(subagents): make delegation-ledger registration tests config-free; refresh ultra replay golden for delegations channel * refactor(subagents): derive TERMINAL_STATUSES from SUBAGENT_STATUS_VALUES + pin it Make thread_state's TERMINAL_STATUSES a frozenset over the status contract's SUBAGENT_STATUS_VALUES instead of a hardcoded literal, so the terminal-status set can never drift from the contract. Add a pinning test asserting the derivation and that the non-terminal "in_progress" stays excluded. Addresses PR #3877 review. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
Zhipeng Zheng
|
fde6885aea
|
fix(agents): coalesce SystemMessages before LLM request (#3711)
DynamicContextMiddleware (PR #3630, p0) uses the ID-swap technique to inject a SystemMessage(reminder) into the middle of the conversation. create_agent then prepends the static system_prompt as another SystemMessage at request time, so strict OpenAI-compatible backends (vLLM, SGLang, Qwen) and Anthropic reject the request with 'System message must be at the beginning'. New SystemMessageCoalescingMiddleware runs in wrap_model_call — after create_agent prepends system_prompt — and merges every SystemMessage into a single leading one before the request reaches the provider. Non-system messages keep their original order; the merged SystemMessage preserves the id of the first system message. Only the request payload is touched; checkpoint state is unchanged, so every consumer that scans history (memory builder, journal, summarization, dynamic-context detection) keeps working. Mirrors the per-request coalescing already done for Claude in claude_provider._coalesce_system_messages (PR #3702) but at a provider-agnostic layer so every backend benefits from a single fix. Closes #3707 |
||
|
Chetan Sharma
|
78fff5a5e2
|
feat(middleware): add TokenBudgetMiddleware for per-run token budget e… (#3412)
* eat(middleware): add TokenBudgetMiddleware for per-run token budget enforcement * address copilot comments * resolve feedback --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
DanielWalnut
|
16391e35ab
|
fix(skills): harden slash skill activation across chat channels (#3466)
* support slash skill activation * format slash skill activation * Preserve slash skill activation with uploads * Address slash skill review feedback * Address slash skill follow-up review * Fix lazy slash skill storage resolution * Keep slash skill activation out of system prompt * Address slash skill review issues * fix: harden slash skill command handling * feat(frontend): add slash skill autocomplete * fix: address slash skill review feedback * fix: preserve slash skill text for IM uploads |
||
|
AochenShen99
|
0fb18e368c
|
refactor(lead-agent): make build_middlewares public to drop the last cross-module private import (#3458)
`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 |
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AochenShen99
|
3b6dd0a4e3
|
feat(subagents): extend deferred MCP tool loading to subagents (#3432)
* feat(subagents): extend deferred MCP tool loading to subagents (#3341) Subagents now reuse the lead agent's deferred-tool path: when tool_search.enabled, MCP tool schemas are withheld from the model and surfaced by name in <available-deferred-tools>, fetched on demand via the generated tool_search helper. DeferredToolFilterMiddleware deterministically rewrites request.tools to hide the deferred schemas (the prompt section is discovery only, not enforcement). Consolidates the assembly into deerflow.tools.builtins.tool_search, now the single home for both assemble_deferred_tools (centralized fail-closed guard, replacing the lead-only private _assemble_deferred) and the relocated get_deferred_tools_prompt_section. Shared by every build path: lead agent, embedded client, and subagent executor. tool_search is appended after the subagent's name-level tool policy and is treated as infrastructure: its catalog is built from the already policy-filtered list, so it can never surface a tool the policy denied. Follow-up to #3370. Fixes #3341. * test(subagents): assert the real middleware builder emits a working deferred filter (#3341) The existing recipe test hand-constructs DeferredToolFilterMiddleware, so it cannot catch a regression in how build_subagent_runtime_middlewares (the call executor._create_agent actually makes) wires the deferred setup into the filter. Add a test that sources the filter from the real builder given a real setup and runs it through a graph: a wrong catalog hash would silently stop promotion, a dropped filter would stop hiding — both now caught. Running the full real middleware stack is intentionally avoided (the other runtime middlewares need sandbox/thread infra to execute, which would make the test flaky); their attachment + ordering before Safety stays locked in test_tool_error_handling_middleware.py. * test(subagents): keep executor tests config-free in CI * chore: trigger ci * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> |
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AochenShen99
|
2bbc7879fa
|
refactor(tool-search): consolidate MCP metadata tag and harden deferred-tool setup (#3370)
Follow-up to #3342 (deferred MCP tool loading). Maintainability cleanup plus hardening of malformed/empty tool_search queries; no change to the deferral mechanism or search ranking. - Add deerflow/tools/mcp_metadata.py as the single source of truth for the "deerflow_mcp" tag (MCP_TOOL_METADATA_KEY + tag_mcp_tool + public is_mcp_tool). Removes the duplicated magic string and the private, cross-module _is_mcp_tool import. - tool_search.search: never raise on model-generated input. Extract _compile_catalog_regex (shared compile-with-literal-fallback); return empty for empty/whitespace queries and a bare "+" instead of matching everything or raising IndexError. - DeferredToolSetup: document the empty-vs-populated invariant. - build_deferred_tool_setup: comment the two distinct empty-return branches. - _assemble_deferred: add return type, rename local to deferred_setup, build the final list with an explicit append. - Tests: use tag_mcp_tool instead of per-file tag helpers; cover empty and bare-"+" queries. |
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AochenShen99
|
d9f4724950
|
fix(tool-search): reliably hide deferred MCP schemas by removing the ContextVar (closures + graph state) (#3342)
* feat(tool-search): add hash-scoped promoted state to ThreadState * feat(tool-search): add immutable DeferredToolCatalog with stable hash * feat(tool-search): add build_deferred_tool_setup + Command-writing tool_search * refactor(tool-search): replace deferred-tool ContextVar with closures + graph state (#3272) Build the deferred catalog + tool_search tool per agent from the policy-filtered tool list (after skill allowed-tools), pass deferred_names + catalog_hash explicitly to DeferredToolFilterMiddleware and the prompt, and record promotions in ThreadState.promoted (scoped by catalog_hash) via a Command-returning tool_search. Removes DeferredToolRegistry and the _registry_var ContextVar so deferral no longer depends on build/execute sharing an async context. MCP tools are tagged with metadata[deerflow_mcp]; client.py assembles deferral the same way. Catalog is built AFTER tool-policy filtering (no policy-excluded tool can leak via tool_search) and assembly is fail-closed. Migrate tests off the deleted registry APIs; delete the obsolete ContextVar-based #2884 regression (re-covered by state-based tests in a follow-up). * test(tool-search): lock tool_search promotion into next model turn via graph state * test(tool-search): cross-context, policy-leak, fail-closed, #2884 isolation regressions * test(tool-search): align real-LLM e2e with closure-based deferred setup * docs: update DeferredToolFilterMiddleware description for closure+state design * style(tests): drop unused import in test_deferred_setup (ruff) * test(tool-search): harden merge_promoted + replace tautological catalog test From independent code review: - merge_promoted: use existing.get("catalog_hash") so a forward-incompatible or externally-injected persisted promoted dict triggers a replace instead of a KeyError crash; add regression test for the malformed-existing case. - test_deferred_catalog: replace the `== [] or True` tautology (a test that could never fail) with a deterministic invalid-regex->literal-fallback check (positive match on calc + negative empty match). - DeferredToolCatalog: comment why frozen-without-slots is required for the cached_property hash/names fields (adding slots=True would break them). * fix(tool-search): read tool_search.enabled from self._app_config in client DeerFlowClient._ensure_agent called get_app_config() directly to read tool_search.enabled, but the client already resolves and stores its config as self._app_config at construction (and uses it everywhere else). The bare call re-resolves config from disk at agent-build time, which raises FileNotFoundError in environments without a config.yaml (CI) — test_client.py's fixture only patches get_app_config during __init__, so the later call hit the real loader. Use self._app_config, matching the rest of the client. * test(tool-search): lock tool_search post-policy append ordering tool_search is appended after skill-allowlist filtering, so the allowlist can no longer deny it by name. Lock the intended contract: it only appears when allowed MCP tools survive the filter, and its catalog (derived from the already policy-filtered list) can never expose a denied tool. Addresses the ordering observation from the Copilot review on #3342. |
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|
Xinmin Zeng
|
be0eae9825
|
fix(runtime): suppress tool execution when provider safety-terminates with tool_calls (#3035)
* fix(runtime): suppress tool execution when provider safety-terminates with tool_calls When a provider stops generation for safety reasons (OpenAI/Moonshot finish_reason=content_filter, Anthropic stop_reason=refusal, Gemini finish_reason=SAFETY/BLOCKLIST/PROHIBITED_CONTENT/SPII/RECITATION/ IMAGE_SAFETY/...), the response may still carry truncated tool_calls. LangChain's tool router treats any non-empty tool_calls as executable, so partial arguments (e.g. write_file with a half-finished markdown) get dispatched and the agent loops on retry. Add SafetyFinishReasonMiddleware at after_model: detect safety termination via a pluggable detector registry, clear both structured tool_calls and raw additional_kwargs.tool_calls / function_call, preserve response_metadata.finish_reason for downstream observers, stamp additional_kwargs.safety_termination for traces, append a user-facing explanation to message content (list-aware for thinking blocks), and emit a safety_termination custom stream event so SSE consumers can reconcile any "tool starting..." UI. Default detectors cover OpenAI-compatible content_filter, Anthropic refusal, and Gemini safety enums (text + image). Custom providers are added via reflection (same pattern as guardrails). Wired into both lead-agent and subagent runtimes. Closes #3028 * fix(runtime): persist safety_termination as a middleware audit event Address review on #3035: the SSE custom event is great for live consumers but invisible to post-run audit. RunEventStore should carry its own row so operators can answer "which runs were safety-suppressed today?" from a single SQL query without joining the message body. Worker now exposes the run-scoped RunJournal via runtime.context["__run_journal"] (sentinel key, internal channel). SafetyFinishReasonMiddleware calls the previously-unused RunJournal.record_middleware, which emits event_type = "middleware:safety_termination" category = "middleware" content = {name, hook, action, changes={ detector, reason_field, reason_value, suppressed_tool_call_count, suppressed_tool_call_names, suppressed_tool_call_ids, message_id, extras}} Tool *arguments* are deliberately excluded — those are the very content the provider filtered and persisting them would defeat the purpose of the safety filter (per review note in #3035). Graceful skips when journal is absent (subagent runtime, unit tests, no-event-store local dev). Journal exceptions never propagate into the agent loop. Refs #3028 * fix(runtime): satisfy ruff format + address Copilot review - ruff format on safety_finish_reason_config.py and e2e demo (CI lint failed on ruff format --check; backend Makefile lint target runs ruff check AND ruff format --check). - Docstring on SafetyFinishReasonConfig now says resolve_variable to match the actual loader used in from_config (the wording was resolve_class previously; behavior is unchanged — resolve_variable mirrors how guardrails.provider is loaded). - Switch the AIMessage type check in SafetyFinishReasonMiddleware._apply from getattr(last, "type") == "ai" to isinstance(last, AIMessage), matching TokenUsageMiddleware / TodoMiddleware / ViewImageMiddleware / SummarizationMiddleware which are the dominant pattern. Refs #3028 |
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|
Xinmin Zeng
|
df95154282
|
fix(tracing): propagate session_id and user_id into Langfuse traces (#2944)
* fix(tracing): propagate session_id and user_id into Langfuse traces
Adds Langfuse v4 reserved trace attributes (langfuse_session_id,
langfuse_user_id, langfuse_trace_name, langfuse_tags) to
RunnableConfig.metadata inside the run worker, so the langchain
CallbackHandler can lift them onto the root trace.
- New deerflow.tracing.metadata.build_langfuse_trace_metadata() returns
the reserved keys when Langfuse is in the enabled providers, else {}.
- worker.run_agent merges them with setdefault so caller-supplied keys
win, allowing per-request overrides from upstream metadata.
- session_id mirrors the LangGraph thread_id; user_id reads
get_effective_user_id() (falls back to "default" in no-auth mode).
- trace_name defaults to "lead-agent"; tags carry env and model name
when DEER_FLOW_ENV (or ENVIRONMENT) and a model name are present.
Closes #2930
* fix(tracing): attach Langfuse callback at graph root so metadata propagates
The first commit injected ``langfuse_session_id`` / ``langfuse_user_id`` /
``langfuse_trace_name`` / ``langfuse_tags`` into ``RunnableConfig.metadata``,
but on ``main`` the Langfuse callback is attached at *model* level
(``models/factory.py``). LangChain still threads ``parent_run_id`` through
the contextvar, so the handler sees the model as a nested observation and
``__on_llm_action`` strips the ``langfuse_*`` keys
(``keep_langfuse_trace_attributes=False``). The trace's top-level
``sessionId`` / ``userId`` therefore stayed empty in deer-flow's LangGraph
runtime — confirmed live against a real Langfuse instance.
This commit moves the callback to the **graph invocation root** so the
handler fires ``on_chain_start(parent_run_id=None)`` and runs the
``propagate_attributes`` path that actually lifts ``session_id`` /
``user_id`` onto the trace:
- ``models/factory.py``: add ``attach_tracing`` keyword (default ``True``)
so standalone callers (``MemoryUpdater``, etc.) keep their direct
model-level tracing.
- ``agents/lead_agent/agent.py``: call ``build_tracing_callbacks()`` once
inside ``_make_lead_agent`` and append the result to
``config["callbacks"]``; the four in-graph ``create_chat_model`` sites
(bootstrap, default agent, sync + async summarization) pass
``attach_tracing=False`` to avoid duplicate spans.
- ``agents/middlewares/title_middleware.py``: same ``attach_tracing=False``
for the title-generation model, since it inherits the graph's
RunnableConfig via ``_get_runnable_config``.
Test updates:
- ``tests/test_lead_agent_model_resolution.py`` and
``tests/test_title_middleware_core_logic.py``: extend the fake
``create_chat_model`` signatures / mock assertions to accept the new
``attach_tracing`` kwarg.
- ``tests/test_worker_langfuse_metadata.py``: switch the no-user fallback
test from direct ContextVar mutation to ``monkeypatch.setattr`` on
``get_effective_user_id`` to avoid pollution across the langfuse OTel
global tracer provider.
- ``tests/conftest.py``: add an autouse fixture that resets
``deerflow.config.title_config._title_config`` to its pristine default
after every test. Any test that loads the real ``config.yaml`` (via
``get_app_config()``) calls ``load_title_config_from_dict`` and mutates
the module-level singleton, which previously poisoned the
title-middleware suite when run after, e.g., the new
``test_worker_langfuse_metadata.py`` cases. The fixture is independent
of this PR's main change but unblocks the cross-file test run.
Live verification (same Langfuse instance as before):
- Drove ``worker.run_agent`` against the real ``make_lead_agent`` +
``gpt-4o-mini`` for three distinct ``user_context`` identities
(``fancy-engineer``, ``alice-pm``, ``bob-designer``).
- Each run produced one ``lead-agent`` trace whose top-level
``sessionId`` / ``userId`` / ``tags`` carry the expected values, e.g.
``session=e2e-2930-8f347c-alice-pm user=alice-pm name='lead-agent'
tags=['model:gpt-4o-mini']``.
Refs #2930.
* fix(tracing): extend root-callback + metadata injection to the embedded client
Addresses Copilot review on PR #2944.
Commit 2 disabled model-level tracing for ``TitleMiddleware`` and
``_create_summarization_middleware`` because ``_make_lead_agent`` now
attaches the tracing callbacks at the graph invocation root. But the
embedded ``DeerFlowClient`` does not call ``_make_lead_agent`` — it
calls ``_build_middlewares`` directly and never appends the tracing
handlers to its ``RunnableConfig``. So under the embedded path,
title-generation and summarization LLM calls were left untraced —
a regression introduced by this PR.
This commit mirrors the gateway worker's injection in
``DeerFlowClient.stream``:
- Append ``build_tracing_callbacks()`` to ``config["callbacks"]`` so
the Langfuse handler sees ``on_chain_start(parent_run_id=None)`` at
the graph root and runs the ``propagate_attributes`` path.
- Merge ``build_langfuse_trace_metadata(...)`` into
``config["metadata"]`` with ``setdefault`` so caller-supplied keys
still win.
- ``_ensure_agent`` now creates its main model with
``attach_tracing=False`` to avoid duplicate spans now that the
callback lives at the graph root.
Docs:
- ``backend/CLAUDE.md`` Tracing section rewritten to describe the
graph-root attachment model (replacing the inaccurate
"at model-creation time" wording).
- ``README.md`` Langfuse section now lists both injection points
(worker + client) instead of only the worker path.
Tests:
- ``tests/test_client_langfuse_metadata.py`` (new, 3 cases):
callbacks + metadata are injected when Langfuse is enabled,
caller-supplied metadata overrides win via ``setdefault``, and the
injection is inert when Langfuse is disabled.
Live verification on the real Langfuse instance:
=== user=fancy-client ===
id=cbd22847.. session=client-2930-6b9491-fancy-client user=fancy-client name='lead-agent'
=== user=alice-client ===
id=b4f6f576.. session=client-2930-6b9491-alice-client user=alice-client name='lead-agent'
Refs #2930.
* refactor(tracing): address maintainer review on PR #2944
Addresses @WillemJiang's 5 comments.
1. Duplicated metadata-injection code between worker.py and client.py
New ``deerflow.tracing.inject_langfuse_metadata(config, ...)`` helper
takes the 10-line build + merge + setdefault logic that was duplicated
in ``runtime/runs/worker.py`` and ``client.py``. Both callers now share
a single source of truth, so the two paths cannot drift.
2. Direct private-attribute mutation in conftest.py and tests
Added public ``reset_tracing_config()`` / ``reset_title_config()``
functions. ``tests/conftest.py`` and every test that previously did
``tracing_module._tracing_config = None`` or
``title_module._title_config = TitleConfig()`` now goes through the
public API. A future internal rename will surface as an ImportError
instead of a silent no-op.
3. client.py reading os.environ directly
``DeerFlowClient.__init__`` grows an optional ``environment`` parameter
so programmatic callers can pass the deployment label explicitly.
``stream()`` consults ``self._environment`` first and only falls back
to ``DEER_FLOW_ENV`` / ``ENVIRONMENT`` env vars when nothing was
passed in. Backwards compatible — env-var behaviour preserved for
callers that opt to keep using it.
4. build_tracing_callbacks() cached on hot path
Not implemented. Inspected the langfuse v4 ``langchain.CallbackHandler``
constructor: it only resolves the module-level singleton client via
``get_client()`` and initialises a few dicts (no I/O, no env parsing
at construction time). The build is essentially free. Caching would
trade a non-measurable speedup for two real risks: handler instances
carry per-run state internally (``_run_states``, ``_root_run_states``,
``last_trace_id``), and tracing config can be reloaded by env-var
changes between runs. Will revisit if profiling ever shows it as
a hot spot.
5. attach_tracing=False easy to forget at new in-graph call sites
- Module docstring at the top of ``lead_agent/agent.py`` documents
the invariant ("every in-graph ``create_chat_model`` MUST pass
``attach_tracing=False``") and enumerates the current sites.
- New regression test
``test_make_lead_agent_attaches_tracing_callbacks_at_graph_root`` in
``tests/test_lead_agent_model_resolution.py`` locks both halves of
the invariant: ``config["callbacks"]`` carries the tracing handler
after ``_make_lead_agent``, AND every ``create_chat_model`` call
captured by the test passes ``attach_tracing=False``. A future
in-graph site that forgets the flag will fail this test.
Lint clean. Full touched-suite bundle: 246 passed.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
|
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|
DanielWalnut
|
c1b7f1d189
|
feat: static system prompt with DynamicContextMiddleware for prefix-cache optimization (#2801)
* feat(middleware): inject dynamic context via DynamicContextMiddleware
Move memory and current date out of the system prompt and into a
dedicated <system-reminder> HumanMessage injected once per session
(frozen-snapshot pattern) via a new DynamicContextMiddleware.
This keeps the system prompt byte-exact across all users and sessions,
enabling maximum Anthropic/Bedrock prefix-cache reuse.
Key design decisions:
- ID-swap technique: reminder takes the first HumanMessage's ID
(replacing it in-place via add_messages), original content gets a
derived `{id}__user` ID (appended after). Preserves correct ordering.
- hide_from_ui: True on reminder messages so frontend filters them out.
- Midnight crossing: date-update reminder injected before the current
turn's HumanMessage when the conversation spans midnight.
- INFO-level logging for production diagnostics.
Also adds prompt-caching breakpoint budget enforcement tests and
updates ClaudeChatModel docs to reference the new pattern.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(token-usage): log input/output token detail breakdown in middleware
Extend the LLM token usage log line to include input_token_details and
output_token_details (cache_creation, cache_read, reasoning, audio, etc.)
when present. Adds tests covering Anthropic cache detail logging from
both usage_metadata and response_metadata.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: fix nginx
* fix(middleware): always inject date; gate memory on injection_enabled
Date injection is now unconditional — it is part of the static system
prompt replacement and should always be present. Memory injection
remains gated by `memory.injection_enabled` in the app config.
Previously the entire DynamicContextMiddleware was skipped when
injection_enabled was False, which also suppressed the date.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(lint): format files and correct test assertions for token usage middleware
- ruff format dynamic_context_middleware.py and test_claude_provider_prompt_caching.py
- Remove unused pytest import from test_dynamic_context_middleware.py
- Fix two tests that asserted response_metadata fallback logic that
doesn't exist: replace with tests that match actual middleware behavior
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(middleware): address Copilot review comments on DynamicContextMiddleware
- Use additional_kwargs flag for reminder detection instead of content
substring matching, so user messages containing '<system-reminder>'
are not mistakenly treated as injected reminders
- Generate stable UUID when original HumanMessage.id is None to prevent
ambiguous 'None__user' derived IDs and message collisions
- Downgrade per-turn no-op log to DEBUG; keep actual injection events at INFO
- Add two new tests: missing-id UUID fallback and user-text false-positive
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
|
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|
Tao Liu
|
daa3ffc29b
|
feat(loop-detection): make loop detection configurable with per-tool frequency overrides (#2711)
* Make loop detection configurable Expose LoopDetectionMiddleware thresholds through config.yaml while preserving existing defaults and allowing the middleware to be disabled. Refs bytedance/deer-flow#2517 * feat(loop-detection): add per-tool tool_freq_overrides to Phase 1 Adds ToolFreqOverride model and tool_freq_overrides field to LoopDetectionConfig, wires it through LoopDetectionMiddleware, and documents the option in config.example.yaml. Resolves the gap flagged in the #2586 review: without per-tool overrides, users hit by #2510/#2511 (RNA-seq workflows exceeding the bash hard limit) had no way to raise thresholds for one tool without loosening the global limit for every tool. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * docs(loop-detection): document tool_freq_overrides in LoopDetectionMiddleware docstring Add the missing Args entry for tool_freq_overrides, explaining the (warn, hard_limit) tuple structure and how per-tool thresholds supersede the global tool_freq_warn / tool_freq_hard_limit for named tools. Also run ruff format on the three files flagged by the lint check. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(loop-detection): validate LoopDetectionMiddleware __init__ params eagerly Raise clear ValueError at construction time instead of crashing at unpack-time inside _track_and_check when bad values are passed: - tool_freq_overrides: must be 2-tuples of positive ints with hard_limit >= warn - scalar thresholds: warn_threshold, hard_limit, tool_freq_warn, tool_freq_hard_limit must be >= 1 and hard limits must >= their warn pairs - window_size, max_tracked_threads must be >= 1 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(test): isolate credential loader directory-path test from real ~/.claude The test didn't monkeypatch HOME, so on any machine with real Claude Code credentials at ~/.claude/.credentials.json the function fell through to those credentials and the assertion failed. Adding HOME redirect ensures the default credential path doesn't exist during the test. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * style(test): add blank lines after import pytest in TestInitValidation Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(loop-detection): collapse dual validation to LoopDetectionConfig Modifications - LoopDetectionMiddleware.__init__: stripped of all ValueError raises; becomes a plain field-assignment constructor. - LoopDetectionMiddleware.from_config: classmethod that builds the middleware from a Pydantic-validated LoopDetectionConfig and handles the ToolFreqOverride -> tuple[int, int] conversion. - agents/factory.py: SDK construction routed through LoopDetectionMiddleware.from_config(LoopDetectionConfig()) so the defaults path is Pydantic-validated too. - agents/lead_agent/agent.py: uses from_config instead of unpacking config fields by hand. - tests/test_loop_detection_middleware.py: deleted TestInitValidation (16 methods exercising the removed __init__ checks); added TestFromConfig (4 tests: scalar field mapping, override tuple conversion, empty overrides, behavioral smoke test). Result: one validation layer (Pydantic), zero duplication, no __new__ hacks. Both production construction sites flow through LoopDetectionConfig. Test results make test -> 2977 passed, 18 skipped, 0 failed (137s) make format -> All checks passed; 411 files left unchanged * feat(agents): make loop_detection configurable in create_deerflow_agent Adds a `loop_detection: bool | AgentMiddleware = True` field to RuntimeFeatures, mirroring the existing pattern used by `sandbox`, `memory`, and `vision`. SDK users can now disable LoopDetectionMiddleware or replace it with a custom instance built from their own LoopDetectionConfig — e.g. `LoopDetectionMiddleware.from_config(my_cfg)` — instead of being stuck with the hardcoded defaults previously installed by the SDK factory. The lead-agent path (which already reads AppConfig.loop_detection) is unchanged, and the default `True` preserves prior always-on behavior for all existing callers. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> --------- Co-authored-by: knight0940 <631532668@qq.com> Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com> Co-authored-by: Amorend <142649913+knight0940@users.noreply.github.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
AochenShen99
|
cef4224381
|
fix(skills): enforce allowed-tools metadata (#2626)
* fix(skills): parse allowed-tools frontmatter * fix(skills): validate allowed-tools metadata * fix(skills): add shared allowed-tools policy * fix(subagents): enforce skill allowed-tools * fix(agent): enforce skill allowed-tools * refactor(skills): dedupe TypeVar and reuse cached enabled skills - Drop redundant module-level TypeVar in tool_policy; rely on PEP 695 syntax. - Expose get_cached_enabled_skills() and have the lead agent reuse it instead of synchronously rescanning skills on every request. * fix(agent): expose config-scoped skill cache * fix(subagents): pass filtered tools explicitly * fix(skills): clean allowed-tools policy feedback |
||
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yangzheli
|
59c4a3f0a4
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feat(agent): add custom-agent self-updates with user isolation (#2713)
* 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> |
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greatmengqi
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8ba01dfd83
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refactor: thread app_config through lead and subagent task path (#2666)
* 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> |