mirror of
https://github.com/bytedance/deer-flow.git
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64e499be98
* feat(memory): redact queued memory payloads (pii_redaction follow-up) Closes the remaining vector from #3190 (memory storage): request-scoped redaction leaves originals in thread state, and MemoryMiddleware queued that raw conversation into the durable extraction buffer — so the extraction model's input and the persisted facts carried raw PII even with pii_redaction.enabled. - MemoryMiddleware gains pii_redaction_config (same pattern as summarization / durable-context / title); the payload returned by _resolve_add_args() is redacted at the enqueue boundary before add()/aadd(), covering both after_agent and aafter_agent - Reuses the shared redact_text() seam from #5527 via active_pii_detectors/_Redactor/_redact_content; the buffered payload itself becomes clean, not just the extraction model's view - Wired at the lead-agent assembly site - Tests: queue-payload redaction enabled/disabled, detector toggles, message immutability, token stability across turns * fix(memory): reserve existing placeholder indices across the queued batch Review round 1 on #5577: thread state mixes raw user turns with already-redacted tool results and summaries, so a token in a later message must not collide with a new value in an earlier one. Pre-scan the whole queued batch with redactor.reserve() before allocating. Also extend the two MemoryMiddleware stubs in test_lead_agent_model_resolution.py to accept the new pii_redaction_config kwarg (CI backend-unit-tests red). * fix(memory): redact the compaction-triggered memory_flush_hook queue too Review round 2 on #5577: with memory + summarization enabled, memory_flush_hook() queues list(event.messages_to_summarize) directly into MemoryManager.add_nowait(), bypassing MemoryMiddleware entirely — and compaction removes those messages from state right after, so the after-agent redaction can never repair a raw batch queued there. Extract the batch redaction (reserve pre-scan + rebuild) into a shared redact_queued_messages() used by both MemoryMiddleware's enqueue boundary and the hook; the hook gains an optional pii_redaction_config wired via functools.partial at the summarization factory (subagents use skip_memory_flush=True and are unaffected). Regression covers the hook path enabled/disabled/absent. * test(summarization): unwrap the partial in the factory hook identity check edd916e wrapped memory_flush_hook in functools.partial to carry the pii_redaction config, but missed committing the matching update to test_factory_attaches_memory_flush_hook_by_default — a functools.partial is not equal to its wrapped function, so the identity assertion failed deterministically (caught by static review on #5577). * fix(memory): cover tool-call arguments and lenient text blocks in queued payloads Review round 4 on #5577 — two reproduced gaps still allowed raw PII into memory payloads with pii_redaction.enabled=true: - OpenViking retains the full message object, so parsed tool_calls args and provider-format arguments in additional_kwargs bypassed the content-only redaction; queued payloads now redact tool-call args recursively (and reserve them in the pre-scan). - DeerMem's format_conversation_for_update reads p.get("text") of any dict block, not only type=="text" — _redact_content now redacts every string-valued text field so a lenient consumer cannot bypass it. Both repro shapes are pinned as regressions. * fix(pii): value-derived placeholders for stable capture identity Review round 5 on #5577: sequential allocation is order-dependent — the same unchanged message re-redacted in a later batch can get a different token, which flips OpenViking's content-signature and replays already recorded turns into persistent memory sessions. Placeholders are now a pure function of the matched value (sha256-6 per category): identities stay stable across batches, enqueues, seams, and backends with no shared state and no stored mapping. - _Redactor becomes stateless; the reserve machinery (index pre-scans, _PLACEHOLDER_PATTERN) is removed along with its call sites - redact_texts keeps its signature, now a simple per-text redaction - reserve-semantics tests are superseded by cross-enqueue identity tests (two successive captures where the second batch carries higher existing placeholders); the compiled-graph echo regex matches the new format * fix(pii): 128-bit placeholder digests; redact tool-argument keys Review round 6 on #5577: - [P2] The 24-bit truncation collided distinct identities (reproduced: contact3513@ / contact3727@example.com both rendered [EMAIL_575dbb]). Retain 128 bits of the digest so separate people stay separate at any realistic volume; the compiled-graph echo regex and the AGENTS.md entry follow the new format. - [P2] _redact_strings redacted values but preserved mapping keys, so {"contacts": {"alice@example.com": "manager"}} kept the raw email as a key. Redact string keys along with values; deterministic 128-bit tokens make distinct-key collisions a hash-collision non-event, and a redacted key equal to another key merges the entries. Regression pins the email-keyed arguments shape. * docs(agents): trim PiiRedactionMiddleware entry; drop stale reserve wording The chain grew past the 98304-byte AG002 hard limit again (main growth plus the round-6 format example). Trim the entry to fit, and replace the stale reserve-indices wording with the value-derived semantics that replaced it. * docs(agents): shrink PiiRedactionMiddleware entry below the chain budget main moved (#5609) and the base comparison flipped the nearly-over chain back to an AG002 error (98441 > 98304). Trim the entry to the facts that are not documented elsewhere: seams, allowlist, order, enqueue boundary. * fix(pii): letters-only value-derived tokens; wire the factory SDK path Review round 7 on #5577: - [P2] Hex digests carry digit runs that later digit-anchored detectors re-scan, corrupting minted tokens into nested placeholders (reproduced: user280@example.com -> [EMAIL_…[PHONE_…]…], ~1/140 emails). The 128-bit digest is now encoded over a-z, so a minted token survives the full pinned detector order. - [P2] create_deerflow_agent (the SDK path) constructed MemoryMiddleware and DurableContextMiddleware without pii_redaction_config; the knob is now an explicit optional parameter threaded through _assemble_from_features (the factory reads no config files by design, so SDK callers pass it explicitly). - [Nit] hoist the Command rebuild comprehension out of the per-message loop in _redact_result. - [Nit] drop the dead _iter_tool_calls helper and Iterator import. * test(factory): pin pii_redaction_config wiring; document the parameter Review round 8 on #5577: - [P3] test_create_deerflow_agent.py had zero coverage of the pii_redaction_config parameter; two regressions now assert that create_deerflow_agent(pii_redaction_config=cfg) builds a MemoryMiddleware and a DurableContextMiddleware carrying cfg, and that the default is None (redaction off). - [Nit] document the parameter in the create_deerflow_agent docstring: None leaves redaction off; a middleware= takeover bypasses the assembly that would apply it. * fix(pii): HMAC-scoped placeholder tokens; install model-bound redaction in the SDK chain Review round 10 on #5577: - [P2] sha256(category||value) is an unkeyed, globally deterministic fingerprint: a provider or memory backend can confirm candidate values offline and link identical values across users and threads, contradicting the documented no-re-identification guarantee. Placeholders are now HMAC-derived over a deployment-scoped token_secret (new config field); the docstring documents the linkability scope for both modes. - [P2] _assemble_from_features() never appended PiiRedactionMiddleware, so the SDK knob enabled only the downstream seams while the primary model still received raw content. The middleware is now appended in the same relative position as the lead-agent base chain when the gate is on, with a model-call regression through the assembled SDK chain. * docs(pii): document token_secret; update the placeholder format examples Follow-up to the round-10 review on #5577: - config.example.yaml documents the new token_secret knob: a deployment-scoped secret makes placeholders HMAC-derived and only linkable within one deployment; leaving it unset keeps publicly computable fingerprints (documented trade). - the PiiRedactionConfig class docstring's placeholder example now matches the shipped format (128-bit HMAC digests over base-26 letters). * docs(pii): update the module docstring's placeholder format example The module docstring still showed the sequential [EMAIL_1] example; align it with the shipped value-derived format (128-bit HMAC digests over base-26 letters). * fix(pii): require a non-empty token_secret when redaction is enabled Review round 11 on #5577: with the secret optional, the default enabled configuration minted empty-key HMAC digests — publicly computable, globally linkable fingerprints of the raw values, confirmable by offline guessing for low-entropy identifiers such as phone numbers. - PiiRedactionConfig now rejects enabled=true without a non-empty token_secret (minimum 16 characters); the unkeyed mode is gone entirely. - The request-scoped redactor in _process_request now carries the configured key: it built its redactor without it, so user-message placeholders silently diverged from every other seam for the same value. The empty-key guard in _placeholder_token is the backstop that keeps every path keyed. - Module/config docstrings and config.example.yaml no longer describe the placeholders as irreversible or the secret as optional; the stale reserve-semantics wording and the "memory path not covered" note are corrected. - Regressions: validation errors for missing/blank/short secrets, and the user-message boundary minting the same keyed token as redact_text. * fix(memory): redact original_user_content on queued memory payloads Review round 12 on #5577: UploadsMiddleware preserves the raw user turn in additional_kwargs.original_user_content, but redact_queued_messages rewrote only content, tool_calls, and provider-format tool_calls — the queued copy still carried the raw value, so the memory queue and backend received raw PII with redaction enabled. The shared helper now redacts the provenance string field on the queued copy (same batch redactor, merged with the existing additional_kwargs rewrite, rebuilt via model_copy — thread-state messages stay untouched), covering both the enqueue boundary and the compaction flush hook. Regressions pin the redacted provenance field through both paths and the untouched original. * fix(memory): redact human_input_response on queued memory payloads Review round 13 on #5577: clarification replies keep the raw user answer in additional_kwargs.human_input_response.value (DeerMem reads that mapping via its read_human_input_response mirror), but redact_queued_messages rewrote only content, tool_calls, and original_user_content — the queued copy still carried the raw value into the memory queue and backend. The shared helper now rebuilds that mapping with value redacted (same batch redactor) while preserving the protocol fields (version/kind/source/ request_id/option_id/response_kind), merged with the existing additional_kwargs rewrite and rebuilt via model_copy so thread-state messages stay untouched. Regressions pin the redacted value, preserved protocol fields, the untouched original, and the flush-hook path. * chore(config): bump config_version to 47; generate token_secret on upgrade Review round 13 on #5577: existing deployments can legitimately sit at config_version 46 with pii_redaction.enabled: true and no token_secret; the mandatory-secret validator would fail their startup while config.example.yaml still said 46, so the upgrade checker considered them current and emitted no migration warning. - Bump config_version to 47 so the outdated-config warning fires for v46. - Add a v47 migration to scripts/config-upgrade.sh: when pii_redaction is enabled and token_secret is missing/blank, generate a random deployment- scoped secret (secrets.token_urlsafe) and persist it, so the upgraded deployment starts under the mandatory validation. - Regressions: a v46 enabled config is flagged outdated against the real example; the upgrade run generates a >= 16-char secret and the result passes AppConfig validation; a disabled config gets no secret. - Also switch the two v41 script tests from a hardcoded "bash" to SCRIPT_BASH/skipif, matching the documented Windows constraint and the neighboring tests (hardcoded bash resolves to the WSL stub there). * chore(helm): sync chart config_version to 47 Review round 14 on #5577: check_config_version.sh fails whenever the chart's embedded config_version lags config.example.yaml, and the chart workflow is triggered by config.example.yaml — so the v47 bump left this head failing that check. Update the values.yaml config block and the README example to 47; scripts/check_config_version.sh now passes. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
1610 lines
70 KiB
Python
1610 lines
70 KiB
Python
"""Tests for lead agent runtime model resolution behavior."""
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from __future__ import annotations
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import asyncio
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import inspect
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any
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from unittest.mock import ANY, AsyncMock, MagicMock, create_autospec
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import pytest
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from langchain.agents import create_agent
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from langchain.agents.middleware import AgentMiddleware
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langchain_core.outputs import ChatGeneration, ChatResult
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from langchain_core.tools import tool
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from deerflow.agents.lead_agent import agent as lead_agent_module
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from deerflow.agents.middlewares import summarization_middleware as summarization_middleware_module
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from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
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from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
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from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
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from deerflow.agents.thread_state import DeltaThreadState, ThreadState
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from deerflow.config.agents_config import AgentConfig
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from deerflow.config.app_config import AppConfig
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from deerflow.config.extensions_config import ExtensionsConfig
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from deerflow.config.loop_detection_config import LoopDetectionConfig
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from deerflow.config.memory_config import MemoryConfig
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from deerflow.config.model_config import ModelConfig
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from deerflow.config.sandbox_config import SandboxConfig
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from deerflow.config.subagents_config import SubagentsAppConfig
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from deerflow.config.summarization_config import SummarizationConfig
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from deerflow.runtime.checkpoint_mode import INTERNAL_CHECKPOINT_MODE_KEY
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from deerflow.runtime.secret_context import write_slash_skill_source_path
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from deerflow.skills.types import Skill, SkillCategory
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_POLICY_INTEGRATION_TOOL_CALLS: list[str] = []
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@tool
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def policy_integration_dangerous_tool() -> str:
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"""Record an invocation of a tool that the active skill does not allow."""
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_POLICY_INTEGRATION_TOOL_CALLS.append("executed")
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return "executed"
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class _PolicyBypassModel(BaseChatModel):
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"""Emit a forbidden call even when the bound schema omits it."""
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call_count: int = 0
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bound_tool_names: list[list[str]] = []
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@property
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def _llm_type(self) -> str:
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return "policy-bypass-test"
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def bind_tools(self, tools: Any, **kwargs: Any):
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self.bound_tool_names.append([tool.name for tool in tools])
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return self
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def _generate(self, messages, stop=None, run_manager=None, **kwargs):
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self.call_count += 1
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if self.call_count == 1:
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message = AIMessage(
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content="",
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tool_calls=[
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{
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"id": "forbidden-call",
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"name": policy_integration_dangerous_tool.name,
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"args": {},
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}
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],
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)
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else:
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message = AIMessage(content="done")
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return ChatResult(generations=[ChatGeneration(message=message)])
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class _PolicyStorageStub:
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def __init__(self, skills: list[Skill]):
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self._skills = skills
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def load_skills(self, *, enabled_only: bool = False) -> list[Skill]:
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return [skill for skill in self._skills if skill.enabled or not enabled_only]
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def get_container_root(self) -> str:
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return "/mnt/skills"
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def _make_app_config(models: list[ModelConfig], loop_detection: LoopDetectionConfig | None = None) -> AppConfig:
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return AppConfig(
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models=models,
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sandbox=SandboxConfig(use="deerflow.sandbox.local:LocalSandboxProvider"),
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loop_detection=loop_detection or LoopDetectionConfig(),
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)
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def _make_model(name: str, *, supports_thinking: bool) -> ModelConfig:
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return ModelConfig(
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name=name,
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display_name=name,
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description=None,
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use="langchain_openai:ChatOpenAI",
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model=name,
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supports_thinking=supports_thinking,
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supports_vision=False,
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)
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class ConfiguredGuardMiddleware(AgentMiddleware):
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pass
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class ConfiguredAuditMiddleware(AgentMiddleware):
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pass
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class ConfiguredInitFailureMiddleware(AgentMiddleware):
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def __init__(self) -> None:
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raise RuntimeError("configured middleware init failed")
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class ConfiguredNonMiddleware:
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pass
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def test_make_lead_agent_signature_matches_langgraph_server_factory_abi():
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assert list(inspect.signature(lead_agent_module.make_lead_agent).parameters) == ["config"]
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@pytest.mark.parametrize(
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("reader", "is_subagent", "is_bootstrap", "expected"),
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[
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(None, False, False, False),
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({"allowed_ids": ["other-thread"]}, False, False, False),
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(lambda: None, False, False, True),
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(lambda: None, True, False, False),
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(lambda: None, False, True, False),
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],
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)
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def test_lead_conversation_tool_requires_callable_host_reader(monkeypatch, reader, is_subagent, is_bootstrap, expected):
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import deerflow.tools as tools_module
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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get_available_tools = MagicMock(return_value=[])
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monkeypatch.setattr(tools_module, "get_available_tools", get_available_tools)
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monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", lambda *args, **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda *args, **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "apply_prompt_template", lambda **kwargs: "system prompt")
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
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monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
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monkeypatch.setattr(lead_agent_module, "build_tracing_callbacks", lambda: [])
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lead_agent_module._make_lead_agent(
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{"context": {"__conversation_reader": reader, "is_subagent": is_subagent, "is_bootstrap": is_bootstrap}},
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app_config=app_config,
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)
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kwargs = get_available_tools.call_args.kwargs
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if is_bootstrap:
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assert "include_conversation_reader" not in kwargs
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else:
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assert kwargs["include_conversation_reader"] is expected
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def test_make_lead_agent_uses_server_auth_identity_for_all_user_scoped_inputs(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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captured: dict[str, object] = {}
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import deerflow.tools as tools_module
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def _load_agent_config(name, *, user_id=None):
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captured["agent_config_user_id"] = user_id
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return AgentConfig(name=name)
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def _load_skills(available_skills, *, app_config, user_id=None):
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captured["skills_user_id"] = user_id
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return []
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def _build_middlewares(config, model_name, agent_name=None, **kwargs):
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captured["middleware_user_id"] = kwargs.get("user_id")
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return []
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def _apply_prompt_template(**kwargs):
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captured["prompt_user_id"] = kwargs.get("user_id")
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return "system prompt"
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monkeypatch.setattr(lead_agent_module, "load_agent_config", _load_agent_config)
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monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", _load_skills)
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monkeypatch.setattr(lead_agent_module, "build_middlewares", _build_middlewares)
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monkeypatch.setattr(lead_agent_module, "apply_prompt_template", _apply_prompt_template)
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
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monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
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monkeypatch.setattr(lead_agent_module, "build_tracing_callbacks", lambda: [])
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monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
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lead_agent_module._make_lead_agent(
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{
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"configurable": {
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"agent_name": "researcher",
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"langgraph_auth_user_id": "authenticated-user",
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"user_id": "spoofed-configurable-user",
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},
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"context": {
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"user_id": "spoofed-context-user",
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},
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},
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app_config=app_config,
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)
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assert captured == {
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"agent_config_user_id": "authenticated-user",
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"skills_user_id": "authenticated-user",
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"middleware_user_id": "authenticated-user",
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"prompt_user_id": "authenticated-user",
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}
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def test_make_lead_agent_applies_custom_agent_memory_opt_out(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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app_config.memory = MemoryConfig(enabled=True, mode="tool")
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captured: dict[str, object] = {"memory_tool_appends": 0}
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import deerflow.tools as tools_module
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monkeypatch.setattr(
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lead_agent_module,
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"load_agent_config",
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lambda name, *, user_id=None: AgentConfig(name=name, memory_enabled=False),
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)
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monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", lambda available_skills, *, app_config, user_id=None: [])
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def _build_middlewares(*args, **kwargs):
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captured["middleware_memory_enabled"] = kwargs.get("memory_enabled")
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return []
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def _apply_prompt_template(**kwargs):
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captured["prompt_memory_enabled"] = kwargs.get("memory_enabled")
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return "system prompt"
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def _append_memory_tools(_tools):
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captured["memory_tool_appends"] = int(captured["memory_tool_appends"]) + 1
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monkeypatch.setattr(lead_agent_module, "build_middlewares", _build_middlewares)
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monkeypatch.setattr(lead_agent_module, "apply_prompt_template", _apply_prompt_template)
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monkeypatch.setattr(lead_agent_module, "_append_memory_tools_without_name_conflicts", _append_memory_tools)
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
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monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
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monkeypatch.setattr(lead_agent_module, "build_tracing_callbacks", lambda: [])
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monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
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lead_agent_module._make_lead_agent(
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{"configurable": {"agent_name": "stateless-worker"}},
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app_config=app_config,
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)
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assert captured == {
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"memory_tool_appends": 0,
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"middleware_memory_enabled": False,
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"prompt_memory_enabled": False,
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}
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def test_make_lead_agent_scopes_bootstrap_middlewares_to_custom_agent(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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middleware_calls: list[dict[str, object]] = []
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prompt_calls: list[dict[str, object]] = []
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import deerflow.tools as tools_module
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monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", lambda *args, **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda *args, **kwargs: middleware_calls.append(kwargs) or [])
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monkeypatch.setattr(lead_agent_module, "apply_prompt_template", lambda **kwargs: prompt_calls.append(kwargs) or "system prompt")
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
monkeypatch.setattr(lead_agent_module, "build_tracing_callbacks", lambda: [])
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
|
|
lead_agent_module._make_lead_agent(
|
|
{"configurable": {"agent_name": "game", "is_bootstrap": True}},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert len(middleware_calls) == 1
|
|
assert middleware_calls[0]["agent_name"] == "game"
|
|
assert middleware_calls[0]["available_skills"] == {"bootstrap"}
|
|
assert middleware_calls[0]["owns_agent_skill_projection"] is False
|
|
assert len(prompt_calls) == 1
|
|
|
|
|
|
def test_make_lead_agent_attaches_tracing_callbacks_at_graph_root(monkeypatch):
|
|
"""Regression guard: tracing handlers must be appended to
|
|
``config["callbacks"]`` (graph invocation root), and every in-graph
|
|
``create_chat_model`` call must pass ``attach_tracing=False``.
|
|
|
|
Catches future contributors who forget the flag when adding new
|
|
in-graph model creation, which would silently produce duplicate
|
|
spans and break Langfuse session/user propagation.
|
|
"""
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
sentinel_handler = object()
|
|
monkeypatch.setattr(lead_agent_module, "build_tracing_callbacks", lambda: [sentinel_handler])
|
|
|
|
seen_attach_tracing: list[bool] = []
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
seen_attach_tracing.append(attach_tracing)
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
config: dict = {"configurable": {"model_name": "safe-model"}}
|
|
lead_agent_module._make_lead_agent(config, app_config=app_config)
|
|
|
|
# Handler must land on the graph invocation config so the Langfuse
|
|
# CallbackHandler fires ``on_chain_start(parent_run_id=None)`` and
|
|
# propagates ``session_id`` / ``user_id`` onto the trace.
|
|
assert sentinel_handler in (config.get("callbacks") or []), "build_tracing_callbacks output must be appended to config['callbacks']"
|
|
|
|
# Every in-graph create_chat_model call must opt out of model-level
|
|
# tracing to avoid duplicate spans.
|
|
assert seen_attach_tracing, "_make_lead_agent did not call create_chat_model"
|
|
assert all(flag is False for flag in seen_attach_tracing), f"in-graph create_chat_model must pass attach_tracing=False; got {seen_attach_tracing}"
|
|
|
|
|
|
def test_internal_make_lead_agent_uses_explicit_app_config(monkeypatch):
|
|
app_config = _make_app_config([_make_model("explicit-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
def _raise_get_app_config():
|
|
raise AssertionError("ambient get_app_config() must not be used when app_config is explicit")
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", _raise_get_app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["name"] = name
|
|
captured["app_config"] = app_config
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module._make_lead_agent(
|
|
{"configurable": {"model_name": "explicit-model"}},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert captured == {
|
|
"name": "explicit-model",
|
|
"app_config": app_config,
|
|
}
|
|
assert result["model"] is not None
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("runtime_options", "expected_effort"),
|
|
[
|
|
pytest.param({}, "high", id="request-leaves-effort-unset"),
|
|
pytest.param({"reasoning_effort": "low"}, "low", id="request-chooses-effort"),
|
|
],
|
|
)
|
|
def test_internal_make_lead_agent_builds_model_whose_profile_sets_reasoning_effort(monkeypatch, runtime_options, expected_effort):
|
|
"""The regular lead-agent build forwards ``reasoning_effort`` even when it is
|
|
None, so the real factory must merge it with a profile-level value instead of
|
|
handing the constructor the keyword twice (which raised ``TypeError``)."""
|
|
model = ModelConfig(
|
|
name="effort-model",
|
|
display_name="effort-model",
|
|
description=None,
|
|
use="langchain_openai:ChatOpenAI",
|
|
model="effort-model",
|
|
api_key="test-key",
|
|
reasoning_effort="high",
|
|
supports_thinking=False,
|
|
supports_reasoning_effort=True,
|
|
supports_vision=False,
|
|
)
|
|
app_config = _make_app_config([model])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module._make_lead_agent(
|
|
{"configurable": {"model_name": "effort-model", **runtime_options}},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert result["model"].reasoning_effort == expected_effort
|
|
|
|
|
|
@pytest.mark.parametrize("is_bootstrap", [False, True])
|
|
def test_internal_make_lead_agent_selects_and_normalizes_delta_state(monkeypatch, is_bootstrap):
|
|
app_config = _make_app_config([_make_model("delta-model", supports_thinking=False)])
|
|
middleware = ViewImageMiddleware()
|
|
original_schema = middleware.state_schema
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"build_middlewares",
|
|
lambda config, model_name, agent_name=None, **kwargs: [middleware],
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", lambda *args, **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module._make_lead_agent(
|
|
{
|
|
"configurable": {
|
|
"model_name": "delta-model",
|
|
"is_bootstrap": is_bootstrap,
|
|
INTERNAL_CHECKPOINT_MODE_KEY: "delta",
|
|
}
|
|
},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert result["state_schema"] is DeltaThreadState
|
|
assert result["middleware"][0] is not middleware
|
|
assert middleware.state_schema is original_schema
|
|
|
|
|
|
def test_internal_make_lead_agent_does_not_take_mode_from_runtime_context(monkeypatch):
|
|
app_config = _make_app_config([_make_model("full-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda *args, **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "_load_enabled_available_skills", lambda *args, **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module._make_lead_agent(
|
|
{
|
|
"configurable": {
|
|
"model_name": "full-model",
|
|
INTERNAL_CHECKPOINT_MODE_KEY: "full",
|
|
},
|
|
"context": {INTERNAL_CHECKPOINT_MODE_KEY: "delta"},
|
|
},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert result["state_schema"] is ThreadState
|
|
|
|
|
|
def test_public_make_lead_agent_does_not_take_mode_from_runtime_context(monkeypatch):
|
|
from deerflow.runtime import checkpoint_mode
|
|
|
|
app_config = _make_app_config([_make_model("full-model", supports_thinking=False)])
|
|
captured: dict[str, object] = {}
|
|
|
|
monkeypatch.setattr(checkpoint_mode, "_frozen_checkpoint_channel_mode", None)
|
|
|
|
def _capture(config, *, app_config):
|
|
captured["config"] = config
|
|
captured["app_config"] = app_config
|
|
return lead_agent_module.LeadAgentAssembly(graph=object(), descriptor=object())
|
|
|
|
monkeypatch.setattr(lead_agent_module, "_assemble_lead_agent", _capture)
|
|
config = {
|
|
"configurable": {"model_name": "full-model"},
|
|
"context": {
|
|
"app_config": app_config,
|
|
INTERNAL_CHECKPOINT_MODE_KEY: "delta",
|
|
},
|
|
}
|
|
|
|
lead_agent_module.make_lead_agent(config)
|
|
|
|
assert config["configurable"][INTERNAL_CHECKPOINT_MODE_KEY] == "full"
|
|
assert captured["app_config"] is app_config
|
|
|
|
|
|
def test_make_lead_agent_uses_runtime_app_config_from_context_without_global_read(monkeypatch):
|
|
app_config = _make_app_config([_make_model("context-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
def _raise_get_app_config():
|
|
raise AssertionError("ambient get_app_config() must not be used when runtime context already carries app_config")
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", _raise_get_app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["name"] = name
|
|
captured["app_config"] = app_config
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module.make_lead_agent(
|
|
{
|
|
"context": {
|
|
"model_name": "context-model",
|
|
"app_config": app_config,
|
|
}
|
|
}
|
|
)
|
|
|
|
assert captured == {
|
|
"name": "context-model",
|
|
"app_config": app_config,
|
|
}
|
|
assert result["model"] is not None
|
|
|
|
|
|
def test_resolve_model_name_falls_back_to_default(monkeypatch, caplog):
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("default-model", supports_thinking=False),
|
|
_make_model("other-model", supports_thinking=True),
|
|
]
|
|
)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
|
|
with caplog.at_level("WARNING"):
|
|
resolved = lead_agent_module._resolve_model_name("missing-model")
|
|
|
|
assert resolved == "default-model"
|
|
assert "fallback to default model 'default-model'" in caplog.text
|
|
|
|
|
|
def test_resolve_model_name_uses_default_when_none(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("default-model", supports_thinking=False),
|
|
_make_model("other-model", supports_thinking=True),
|
|
]
|
|
)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
|
|
resolved = lead_agent_module._resolve_model_name(None)
|
|
|
|
assert resolved == "default-model"
|
|
|
|
|
|
def test_resolve_model_name_raises_when_no_models_configured(monkeypatch):
|
|
app_config = _make_app_config([])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="No chat models are configured",
|
|
):
|
|
lead_agent_module._resolve_model_name("missing-model")
|
|
|
|
|
|
def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkeypatch):
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["name"] = name
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
captured["reasoning_effort"] = reasoning_effort
|
|
captured["app_config"] = app_config
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module.make_lead_agent(
|
|
{
|
|
"configurable": {
|
|
"model_name": "safe-model",
|
|
"thinking_enabled": True,
|
|
"is_plan_mode": False,
|
|
"subagent_enabled": False,
|
|
}
|
|
}
|
|
)
|
|
|
|
assert captured["name"] == "safe-model"
|
|
assert captured["thinking_enabled"] is False
|
|
assert captured["app_config"] is app_config
|
|
assert result["model"] is not None
|
|
|
|
|
|
def test_make_lead_agent_reads_runtime_options_from_context(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("default-model", supports_thinking=False),
|
|
_make_model("context-model", supports_thinking=True),
|
|
]
|
|
)
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
get_available_tools = MagicMock(return_value=[])
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", get_available_tools)
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["name"] = name
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
captured["reasoning_effort"] = reasoning_effort
|
|
captured["app_config"] = app_config
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module.make_lead_agent(
|
|
{
|
|
"context": {
|
|
"model_name": "context-model",
|
|
"thinking_enabled": False,
|
|
"reasoning_effort": "high",
|
|
"is_plan_mode": True,
|
|
"subagent_enabled": True,
|
|
"max_concurrent_subagents": 7,
|
|
}
|
|
}
|
|
)
|
|
|
|
assert captured == {
|
|
"name": "context-model",
|
|
"thinking_enabled": False,
|
|
"reasoning_effort": "high",
|
|
"app_config": app_config,
|
|
}
|
|
get_available_tools.assert_called_once_with(model_name="context-model", groups=None, subagent_enabled=True, mcp_plugins=None, include_conversation_reader=False, app_config=app_config, chat_model=result["model"])
|
|
assert result["model"] is not None
|
|
|
|
|
|
@pytest.mark.parametrize("is_bootstrap", [False, True])
|
|
def test_make_lead_agent_filters_clarification_tool_for_non_interactive_runs(monkeypatch, is_bootstrap):
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
def _named_tool(name: str):
|
|
tool = MagicMock()
|
|
tool.name = name
|
|
return tool
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(
|
|
tools_module,
|
|
"get_available_tools",
|
|
lambda **kwargs: [_named_tool("ask_clarification"), _named_tool("bash")],
|
|
)
|
|
captured_prompt_policy = {}
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", create_autospec(lead_agent_module.build_middlewares, return_value=[]))
|
|
|
|
def _capture_prompt_policy(**kwargs):
|
|
captured_prompt_policy["policy"] = kwargs["interaction_policy"]
|
|
return "prompt"
|
|
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"apply_prompt_template",
|
|
_capture_prompt_policy,
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
result = lead_agent_module.make_lead_agent(
|
|
{
|
|
"context": {
|
|
"model_name": "safe-model",
|
|
"thinking_enabled": False,
|
|
"subagent_enabled": False,
|
|
"non_interactive": True,
|
|
"is_bootstrap": is_bootstrap,
|
|
}
|
|
}
|
|
)
|
|
|
|
assert [tool.name for tool in result["tools"]] == (["bash", "setup_agent"] if is_bootstrap else ["bash"])
|
|
assert captured_prompt_policy["policy"].mode.value == "scheduled"
|
|
|
|
|
|
def test_make_lead_agent_rejects_invalid_bootstrap_agent_name(monkeypatch):
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
|
|
with pytest.raises(ValueError, match="Invalid agent name"):
|
|
lead_agent_module.make_lead_agent(
|
|
{
|
|
"configurable": {
|
|
"model_name": "safe-model",
|
|
"thinking_enabled": False,
|
|
"is_plan_mode": False,
|
|
"subagent_enabled": False,
|
|
"is_bootstrap": True,
|
|
"agent_name": "../../../tmp/evil",
|
|
}
|
|
}
|
|
)
|
|
|
|
|
|
def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("stale-model", supports_thinking=False),
|
|
ModelConfig(
|
|
name="vision-model",
|
|
display_name="vision-model",
|
|
description=None,
|
|
use="langchain_openai:ChatOpenAI",
|
|
model="vision-model",
|
|
supports_thinking=False,
|
|
supports_vision=True,
|
|
),
|
|
]
|
|
)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"model_name": "stale-model", "is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="vision-model",
|
|
custom_middlewares=[MagicMock()],
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert any(isinstance(m, lead_agent_module.ViewImageMiddleware) for m in middlewares)
|
|
# verify the custom middleware is injected correctly.
|
|
# With this test's default safety config enabled, the tail order is:
|
|
# ..., custom, TerminalResponseMiddleware, ModelLengthFinishReasonMiddleware,
|
|
# SafetyFinishReasonMiddleware, ClarificationMiddleware, so the custom mock
|
|
# sits at index [-5].
|
|
assert len(middlewares) > 0 and isinstance(middlewares[-5], MagicMock)
|
|
|
|
from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
|
|
from deerflow.agents.middlewares.model_length_finish_reason_middleware import ModelLengthFinishReasonMiddleware
|
|
from deerflow.agents.middlewares.safety_finish_reason_middleware import SafetyFinishReasonMiddleware
|
|
from deerflow.agents.middlewares.terminal_response_middleware import TerminalResponseMiddleware
|
|
|
|
assert isinstance(middlewares[-4], TerminalResponseMiddleware)
|
|
assert isinstance(middlewares[-3], ModelLengthFinishReasonMiddleware)
|
|
assert isinstance(middlewares[-2], SafetyFinishReasonMiddleware)
|
|
assert isinstance(middlewares[-1], ClarificationMiddleware)
|
|
|
|
|
|
def test_build_middlewares_custom_agent_memory_opt_out_keeps_dynamic_date_only(monkeypatch):
|
|
from deerflow.agents.middlewares.dynamic_context_middleware import DynamicContextMiddleware
|
|
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
|
|
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
summarization_kwargs: dict[str, object] = {}
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"_create_summarization_middleware",
|
|
lambda **kwargs: summarization_kwargs.update(kwargs) or None,
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
agent_name="stateless-worker",
|
|
memory_enabled=False,
|
|
app_config=app_config,
|
|
)
|
|
|
|
dynamic_context = next(middleware for middleware in middlewares if isinstance(middleware, DynamicContextMiddleware))
|
|
assert dynamic_context._memory_enabled is False
|
|
assert summarization_kwargs["skip_memory_flush"] is True
|
|
assert not any(isinstance(middleware, MemoryMiddleware) for middleware in middlewares)
|
|
|
|
|
|
def test_build_middlewares_prefers_startup_execution_capacity_after_reload(monkeypatch):
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
app_config.subagent_runtime.max_running = 12
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{
|
|
"configurable": {
|
|
"model_name": "safe-model",
|
|
"is_plan_mode": False,
|
|
"subagent_enabled": True,
|
|
"max_concurrent_subagents": 10,
|
|
}
|
|
},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
subagent_execution_capacity=3,
|
|
)
|
|
|
|
limiter = next(middleware for middleware in middlewares if isinstance(middleware, lead_agent_module.SubagentLimitMiddleware))
|
|
assert limiter.max_concurrent == 3
|
|
|
|
|
|
def test_build_middlewares_passes_explicit_app_config_to_shared_factory(monkeypatch):
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
captured: dict[str, object] = {}
|
|
|
|
def _raise_get_app_config():
|
|
raise AssertionError("ambient get_app_config() must not be used when app_config is explicit")
|
|
|
|
provider = object()
|
|
|
|
def _fake_build_lead_runtime_middlewares(*, app_config, lazy_init, authorization_provider):
|
|
captured["app_config"] = app_config
|
|
captured["lazy_init"] = lazy_init
|
|
captured["authorization_provider"] = authorization_provider
|
|
return ["base-middleware"]
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", _raise_get_app_config)
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"build_lead_runtime_middlewares",
|
|
_fake_build_lead_runtime_middlewares,
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"TitleMiddleware",
|
|
lambda *, app_config, extensions: captured.setdefault("title_app_config", app_config) or "title-middleware",
|
|
)
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"MemoryMiddleware",
|
|
lambda agent_name=None, *, memory_config, pii_redaction_config=None: captured.setdefault("memory_config", memory_config) or "memory-middleware",
|
|
)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
authorization_provider=provider,
|
|
)
|
|
|
|
assert captured == {
|
|
"app_config": app_config,
|
|
"lazy_init": True,
|
|
"authorization_provider": provider,
|
|
"title_app_config": app_config,
|
|
"memory_config": app_config.memory,
|
|
}
|
|
assert middlewares[0] == "base-middleware"
|
|
|
|
|
|
def test_build_middlewares_passes_run_model_name_to_summarization(monkeypatch):
|
|
"""The resolved run model (e.g. a custom agent's model, distinct from models[0])
|
|
is threaded into the summarization factory, so null-model compaction summarizes
|
|
with it rather than config.models[0]. Production-shaped: the model reaches the
|
|
factory as an argument, not via runtime.context."""
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("default-model", supports_thinking=False),
|
|
_make_model("custom-agent-model", supports_thinking=False),
|
|
]
|
|
)
|
|
captured: dict[str, object] = {}
|
|
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init: ["base-middleware"])
|
|
monkeypatch.setattr(
|
|
lead_agent_module,
|
|
"_create_summarization_middleware",
|
|
lambda **kwargs: captured.update(kwargs) or None,
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
monkeypatch.setattr(lead_agent_module, "TitleMiddleware", lambda *, app_config, extensions: "title-middleware")
|
|
monkeypatch.setattr(lead_agent_module, "MemoryMiddleware", lambda agent_name=None, *, memory_config, pii_redaction_config=None: "memory-middleware")
|
|
|
|
lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="custom-agent-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert captured["run_model_name"] == "custom-agent-model"
|
|
|
|
|
|
def test_build_middlewares_orders_skill_activation_before_policy_and_durable_context(monkeypatch):
|
|
from deerflow.agents.middlewares.durable_context_middleware import DurableContextMiddleware
|
|
from deerflow.agents.middlewares.skill_activation_middleware import SkillActivationMiddleware
|
|
from deerflow.agents.middlewares.skill_tool_policy_middleware import SkillToolPolicyMiddleware
|
|
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
activation_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, SkillActivationMiddleware))
|
|
policy_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, SkillToolPolicyMiddleware))
|
|
durable_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, DurableContextMiddleware))
|
|
assert policy_idx == activation_idx + 1
|
|
assert durable_idx == policy_idx + 1
|
|
assert middlewares[activation_idx]._slash_source_owner_token == middlewares[policy_idx]._slash_source_owner_token
|
|
|
|
|
|
@pytest.mark.parametrize("use_stale_path", [False, True], ids=["restrictive-skill", "stale-active-path"])
|
|
def test_compiled_skill_policy_chain_filters_schema_and_blocks_execution(monkeypatch, use_stale_path):
|
|
from deerflow.agents.middlewares.durable_context_middleware import DurableContextMiddleware
|
|
from deerflow.agents.middlewares.skill_activation_middleware import SkillActivationMiddleware
|
|
from deerflow.agents.middlewares.skill_tool_policy_middleware import SkillToolPolicyMiddleware
|
|
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
activation_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, SkillActivationMiddleware))
|
|
durable_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, DurableContextMiddleware))
|
|
compiled_slice = middlewares[activation_idx : durable_idx + 1]
|
|
assert [type(middleware) for middleware in compiled_slice] == [SkillActivationMiddleware, SkillToolPolicyMiddleware, DurableContextMiddleware]
|
|
|
|
skill_dir = Path("/tmp/skills/public/restricted")
|
|
restricted = Skill(
|
|
name="restricted",
|
|
description="Restrictive integration skill",
|
|
license="MIT",
|
|
skill_dir=skill_dir,
|
|
skill_file=skill_dir / "SKILL.md",
|
|
relative_path=Path("restricted"),
|
|
category=SkillCategory.PUBLIC,
|
|
allowed_tools=("read_file",),
|
|
enabled=True,
|
|
)
|
|
policy = compiled_slice[1]
|
|
policy._storage = lambda: _PolicyStorageStub([] if use_stale_path else [restricted])
|
|
|
|
context: dict[str, object] = {}
|
|
active_path = "/mnt/skills/public/missing/SKILL.md" if use_stale_path else restricted.get_container_file_path()
|
|
write_slash_skill_source_path(
|
|
context,
|
|
active_path,
|
|
owner_token=policy._slash_source_owner_token,
|
|
)
|
|
model = _PolicyBypassModel()
|
|
_POLICY_INTEGRATION_TOOL_CALLS.clear()
|
|
graph = create_agent(
|
|
model=model,
|
|
tools=[policy_integration_dangerous_tool],
|
|
middleware=compiled_slice,
|
|
state_schema=ThreadState,
|
|
)
|
|
|
|
result = graph.invoke(
|
|
{"messages": [HumanMessage(content="continue under the active skill")]},
|
|
context=context,
|
|
)
|
|
|
|
# LangChain skips ``bind_tools`` entirely when middleware filters the
|
|
# request to zero schemas. If the forbidden schema survived, this list
|
|
# would contain a binding with ``policy_integration_dangerous_tool``.
|
|
assert model.bound_tool_names == []
|
|
assert _POLICY_INTEGRATION_TOOL_CALLS == []
|
|
blocked = [message for message in result["messages"] if isinstance(message, ToolMessage) and message.tool_call_id == "forbidden-call"]
|
|
assert len(blocked) == 1
|
|
assert blocked[0].status == "error"
|
|
assert "not allowed by the active skill policy" in blocked[0].content
|
|
|
|
|
|
def test_build_middlewares_places_mcp_routing_before_deferred_filter(monkeypatch):
|
|
from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
|
|
from deerflow.agents.middlewares.mcp_routing_middleware import McpRoutingMiddleware
|
|
from deerflow.agents.middlewares.skill_tool_policy_middleware import SkillToolPolicyMiddleware
|
|
from deerflow.agents.middlewares.tool_promotion_audit_middleware import DeferredToolPromotionAuditMiddleware
|
|
from deerflow.tools.builtins.tool_search import DeferredToolSetup
|
|
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)], loop_detection=LoopDetectionConfig(enabled=False))
|
|
routing = McpRoutingMiddleware({"mcp_thing": {"priority": 100, "keywords": ["orders"]}}, "hash123", 3)
|
|
setup = DeferredToolSetup(object(), frozenset({"mcp_thing"}), "hash123")
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
deferred_setup=setup,
|
|
mcp_routing_middleware=routing,
|
|
)
|
|
|
|
routing_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, McpRoutingMiddleware))
|
|
filter_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, DeferredToolFilterMiddleware))
|
|
audit_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, DeferredToolPromotionAuditMiddleware))
|
|
policy_idx = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, SkillToolPolicyMiddleware))
|
|
assert audit_idx < policy_idx
|
|
assert routing_idx < filter_idx
|
|
|
|
|
|
def test_build_middlewares_uses_loop_detection_config(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(
|
|
warn_threshold=7,
|
|
hard_limit=9,
|
|
window_size=30,
|
|
max_tracked_threads=40,
|
|
tool_freq_warn=50,
|
|
tool_freq_hard_limit=60,
|
|
),
|
|
)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
loop_detection = next(m for m in middlewares if isinstance(m, LoopDetectionMiddleware))
|
|
assert loop_detection.warn_threshold == 7
|
|
assert loop_detection.hard_limit == 9
|
|
assert loop_detection.window_size == 30
|
|
assert loop_detection.max_tracked_threads == 40
|
|
assert loop_detection.tool_freq_warn == 50
|
|
assert loop_detection.tool_freq_hard_limit == 60
|
|
|
|
|
|
def test_build_middlewares_omits_loop_detection_when_disabled(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert not any(isinstance(m, LoopDetectionMiddleware) for m in middlewares)
|
|
|
|
|
|
def test_build_middlewares_injects_configured_extension_middlewares(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.extensions = ExtensionsConfig(
|
|
middlewares=[
|
|
f"{__name__}:ConfiguredGuardMiddleware",
|
|
f"{__name__}:ConfiguredAuditMiddleware",
|
|
]
|
|
)
|
|
manual_middleware = MagicMock()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
custom_middlewares=[manual_middleware],
|
|
app_config=app_config,
|
|
)
|
|
|
|
middleware_types = [type(m).__name__ for m in middlewares]
|
|
assert middleware_types[-6:] == [
|
|
"ConfiguredGuardMiddleware",
|
|
"ConfiguredAuditMiddleware",
|
|
"TerminalResponseMiddleware",
|
|
"ModelLengthFinishReasonMiddleware",
|
|
"SafetyFinishReasonMiddleware",
|
|
"ClarificationMiddleware",
|
|
]
|
|
assert middlewares[middleware_types.index("ConfiguredGuardMiddleware") - 1] is manual_middleware
|
|
|
|
|
|
def test_build_middlewares_passes_subagent_total_limit_from_app_config(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.subagents = SubagentsAppConfig(max_total_per_run=7)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": True, "max_concurrent_subagents": 3}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
limit = next(m for m in middlewares if isinstance(m, SubagentLimitMiddleware))
|
|
assert limit.max_concurrent == 3
|
|
assert limit.max_total == 7
|
|
|
|
|
|
def test_build_middlewares_allows_runtime_subagent_total_limit_override(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.subagents = SubagentsAppConfig(max_total_per_run=7)
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
middlewares = lead_agent_module.build_middlewares(
|
|
{
|
|
"configurable": {
|
|
"is_plan_mode": False,
|
|
"subagent_enabled": True,
|
|
"max_concurrent_subagents": 3,
|
|
"max_total_subagents": 5,
|
|
}
|
|
},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
limit = next(m for m in middlewares if isinstance(m, SubagentLimitMiddleware))
|
|
assert limit.max_total == 5
|
|
|
|
|
|
def test_build_middlewares_rejects_invalid_configured_extension_middleware(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.extensions = ExtensionsConfig(middlewares=[f"{__name__}:_make_model"])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
with pytest.raises(ValueError, match="not an instance of type"):
|
|
lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
|
|
def test_build_middlewares_rejects_configured_extension_class_with_wrong_base(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.extensions = ExtensionsConfig(middlewares=[f"{__name__}:ConfiguredNonMiddleware"])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
with pytest.raises(ValueError, match="is not a subclass of AgentMiddleware"):
|
|
lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
|
|
def test_build_middlewares_reraises_configured_extension_instantiation_failure(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.extensions = ExtensionsConfig(middlewares=[f"{__name__}:ConfiguredInitFailureMiddleware"])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
with pytest.raises(RuntimeError, match="configured middleware init failed"):
|
|
lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
|
|
def test_build_middlewares_rejects_missing_configured_extension_module(monkeypatch):
|
|
app_config = _make_app_config(
|
|
[_make_model("safe-model", supports_thinking=False)],
|
|
loop_detection=LoopDetectionConfig(enabled=False),
|
|
)
|
|
app_config.extensions = ExtensionsConfig(middlewares=["definitely_missing_pkg.middlewares_typo:GuardMiddleware"])
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
|
|
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
|
|
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
|
|
|
|
with pytest.raises(ImportError, match="Could not import module definitely_missing_pkg.middlewares_typo"):
|
|
lead_agent_module.build_middlewares(
|
|
{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
|
|
model_name="safe-model",
|
|
app_config=app_config,
|
|
)
|
|
|
|
|
|
def test_create_summarization_middleware_uses_configured_model_alias(monkeypatch):
|
|
app_config = _make_app_config([_make_model("model-masswork", supports_thinking=False)])
|
|
app_config.summarization = SummarizationConfig(enabled=True, model_name="model-masswork")
|
|
app_config.memory = MemoryConfig(enabled=False)
|
|
|
|
from unittest.mock import MagicMock
|
|
|
|
captured: dict[str, object] = {}
|
|
fake_model = MagicMock()
|
|
fake_model.with_config.return_value = fake_model
|
|
|
|
def _fake_create_chat_model(*, name=None, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["name"] = name
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
captured["reasoning_effort"] = reasoning_effort
|
|
captured["app_config"] = app_config
|
|
return fake_model
|
|
|
|
def _raise_get_app_config():
|
|
raise AssertionError("ambient get_app_config() must not be used when app_config is explicit")
|
|
|
|
monkeypatch.setattr(summarization_middleware_module, "get_app_config", _raise_get_app_config)
|
|
monkeypatch.setattr(summarization_middleware_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(summarization_middleware_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
|
|
|
|
middleware = lead_agent_module._create_summarization_middleware(app_config=app_config)
|
|
|
|
assert captured["name"] == "model-masswork"
|
|
assert captured["thinking_enabled"] is False
|
|
assert captured["app_config"] is app_config
|
|
assert middleware["model"] is fake_model
|
|
fake_model.with_config.assert_called_once_with(tags=["middleware:summarize"])
|
|
|
|
|
|
def test_create_summarization_middleware_uses_default_when_unconfigured(monkeypatch):
|
|
"""Null summary config with no supplied run model builds the anchor from the default
|
|
model — now with an explicit name rather than relying on create_chat_model's internal
|
|
default, so the factory has no implicit models[0] dependency. Same resulting model."""
|
|
app_config = _make_app_config([_make_model("default-model", supports_thinking=False)])
|
|
app_config.summarization = SummarizationConfig(enabled=True, model_name=None)
|
|
app_config.memory = MemoryConfig(enabled=False)
|
|
|
|
captured: dict[str, object] = {}
|
|
fake_model = MagicMock()
|
|
fake_model.with_config.return_value = fake_model
|
|
|
|
def _fake_create_chat_model(**kwargs):
|
|
captured.update(kwargs)
|
|
return fake_model
|
|
|
|
monkeypatch.setattr(summarization_middleware_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(summarization_middleware_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
|
|
|
|
middleware = lead_agent_module._create_summarization_middleware(app_config=app_config)
|
|
|
|
assert captured["name"] == "default-model"
|
|
assert captured["thinking_enabled"] is False
|
|
assert captured["app_config"] is app_config
|
|
assert middleware["model"] is fake_model
|
|
assert middleware["anchor_model_name"] == "default-model"
|
|
|
|
|
|
def test_create_summarization_middleware_threads_run_model_name(monkeypatch):
|
|
"""A custom agent's resolved model reaches the middleware as run_model_name (the
|
|
model-ownership source of truth), while configured_model_name stays None for a
|
|
null summarization config."""
|
|
app_config = _make_app_config(
|
|
[
|
|
_make_model("default-model", supports_thinking=False),
|
|
_make_model("custom-agent-model", supports_thinking=False),
|
|
]
|
|
)
|
|
app_config.summarization = SummarizationConfig(enabled=True, model_name=None)
|
|
app_config.memory = MemoryConfig(enabled=False)
|
|
|
|
fake_model = MagicMock()
|
|
fake_model.with_config.return_value = fake_model
|
|
monkeypatch.setattr(summarization_middleware_module, "create_chat_model", lambda **kwargs: fake_model)
|
|
monkeypatch.setattr(summarization_middleware_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
|
|
|
|
middleware = lead_agent_module._create_summarization_middleware(app_config=app_config, run_model_name="custom-agent-model")
|
|
|
|
assert middleware["run_model_name"] == "custom-agent-model"
|
|
assert middleware["configured_model_name"] is None
|
|
|
|
|
|
def test_create_summarization_middleware_uses_frontend_supported_update_key(monkeypatch):
|
|
"""LangGraph update keys use the middleware class name plus hook name."""
|
|
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
app_config.summarization = SummarizationConfig(enabled=True)
|
|
app_config.memory = MemoryConfig(enabled=False)
|
|
|
|
fake_model = MagicMock()
|
|
fake_model.with_config.return_value = fake_model
|
|
monkeypatch.setattr(summarization_middleware_module, "create_chat_model", lambda **kwargs: fake_model)
|
|
|
|
middleware = lead_agent_module._create_summarization_middleware(app_config=app_config)
|
|
|
|
assert middleware is not None
|
|
update_key = f"{type(middleware).__name__}.before_model"
|
|
assert update_key == "DeerFlowSummarizationMiddleware.before_model"
|
|
|
|
|
|
def test_create_summarization_middleware_threads_resolved_app_config_to_model(monkeypatch):
|
|
fallback_app_config = _make_app_config([_make_model("fallback-model", supports_thinking=False)])
|
|
fallback_app_config.summarization = SummarizationConfig(enabled=True, model_name="fallback-model")
|
|
fallback_app_config.memory = MemoryConfig(enabled=False)
|
|
|
|
from unittest.mock import MagicMock
|
|
|
|
captured: dict[str, object] = {}
|
|
fake_model = MagicMock()
|
|
fake_model.with_config.return_value = fake_model
|
|
|
|
def _fake_create_chat_model(*, name=None, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["app_config"] = app_config
|
|
return fake_model
|
|
|
|
monkeypatch.setattr(summarization_middleware_module, "get_app_config", lambda: fallback_app_config)
|
|
monkeypatch.setattr(summarization_middleware_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(summarization_middleware_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
|
|
|
|
lead_agent_module._create_summarization_middleware()
|
|
|
|
assert captured["app_config"] is fallback_app_config
|
|
|
|
|
|
def test_memory_middleware_uses_explicit_memory_config_without_global_read(monkeypatch):
|
|
from deerflow.agents.middlewares import memory_middleware as memory_middleware_module
|
|
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
|
|
|
|
def _raise_get_memory_config():
|
|
raise AssertionError("ambient get_memory_config() must not be used when memory_config is explicit")
|
|
|
|
monkeypatch.setattr(memory_middleware_module, "get_memory_config", _raise_get_memory_config)
|
|
|
|
middleware = MemoryMiddleware(memory_config=MemoryConfig(enabled=False))
|
|
|
|
assert middleware.after_agent({"messages": []}, runtime=MagicMock(context={"thread_id": "thread-1"})) is None
|
|
|
|
|
|
def test_memory_middleware_async_path_uses_async_manager_call(monkeypatch):
|
|
from deerflow.agents.middlewares import memory_middleware as memory_middleware_module
|
|
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
|
|
|
|
manager = SimpleNamespace(aadd=AsyncMock(), add=MagicMock(side_effect=AssertionError("sync add must not run")))
|
|
monkeypatch.setattr(memory_middleware_module, "get_memory_manager", lambda: manager)
|
|
middleware = MemoryMiddleware(memory_config=MemoryConfig(enabled=True))
|
|
runtime = MagicMock(context={"thread_id": "thread-1", "user_id": "user-1"})
|
|
|
|
result = asyncio.run(middleware.aafter_agent({"messages": [HumanMessage(content="hello")]}, runtime=runtime))
|
|
|
|
assert result is None
|
|
manager.aadd.assert_awaited_once()
|
|
assert manager.aadd.await_args.kwargs["user_id"] == "user-1"
|
|
manager.add.assert_not_called()
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Per-agent model settings (issue #4336)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def test_resolve_runtime_option_precedence():
|
|
# request value wins, even when falsy
|
|
assert lead_agent_module._resolve_runtime_option({"thinking_enabled": False}, "thinking_enabled", True, True) is False
|
|
# agent value used when request omits the key
|
|
assert lead_agent_module._resolve_runtime_option({}, "thinking_enabled", True, False) is True
|
|
# default when neither request nor agent set it
|
|
assert lead_agent_module._resolve_runtime_option({}, "thinking_enabled", None, False) is False
|
|
|
|
|
|
def _make_agent_config(**kwargs):
|
|
from deerflow.config.agents_config import AgentConfig
|
|
|
|
return AgentConfig(name="researcher", **kwargs)
|
|
|
|
|
|
def test_make_lead_agent_applies_agent_model_settings(monkeypatch):
|
|
"""A custom agent's model_settings flow into create_chat_model as
|
|
model_overrides, and its thinking/reasoning defaults apply when the request
|
|
omits them (issue #4336)."""
|
|
app_config = _make_app_config([_make_model("agent-model", supports_thinking=True)])
|
|
agent_config = _make_agent_config(
|
|
model="agent-model",
|
|
model_settings={"temperature": 0.2, "max_tokens": 12000},
|
|
thinking_enabled=False,
|
|
reasoning_effort="high",
|
|
)
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda name, *, user_id=None: agent_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
captured["reasoning_effort"] = reasoning_effort
|
|
captured["model_overrides"] = model_overrides
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
lead_agent_module._make_lead_agent({"context": {"agent_name": "researcher"}}, app_config=app_config)
|
|
|
|
assert captured["model_overrides"] == {"temperature": 0.2, "max_tokens": 12000}
|
|
assert captured["thinking_enabled"] is False # from agent config
|
|
assert captured["reasoning_effort"] == "high" # from agent config
|
|
|
|
|
|
def test_request_thinking_overrides_agent_default(monkeypatch):
|
|
"""An explicit request thinking_enabled wins over the agent's default."""
|
|
app_config = _make_app_config([_make_model("agent-model", supports_thinking=True)])
|
|
agent_config = _make_agent_config(model="agent-model", thinking_enabled=False)
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda name, *, user_id=None: agent_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
lead_agent_module._make_lead_agent(
|
|
{"context": {"agent_name": "researcher", "thinking_enabled": True}},
|
|
app_config=app_config,
|
|
)
|
|
|
|
assert captured["thinking_enabled"] is True # request wins over agent's False
|
|
|
|
|
|
@pytest.mark.parametrize("mcp_plugins", [None, [], ["installed-plugin"]])
|
|
def test_empty_allowed_subagents_disables_requested_delegation(monkeypatch, mcp_plugins):
|
|
"""A request switch cannot widen an explicit Custom Agent hard deny."""
|
|
app_config = _make_app_config([_make_model("agent-model", supports_thinking=False)])
|
|
agent_config = _make_agent_config(model="agent-model", allowed_subagents=[])
|
|
agent_config.mcp_plugins = mcp_plugins
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
get_available_tools = MagicMock(return_value=[])
|
|
monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda name, *, user_id=None: agent_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", get_available_tools)
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
config = {
|
|
"context": {
|
|
"agent_name": "researcher",
|
|
"subagent_enabled": True,
|
|
}
|
|
}
|
|
lead_agent_module._make_lead_agent(config, app_config=app_config)
|
|
|
|
get_available_tools.assert_called_once_with(
|
|
model_name="agent-model",
|
|
groups=None,
|
|
mcp_plugins=mcp_plugins,
|
|
subagent_enabled=False,
|
|
include_conversation_reader=False,
|
|
app_config=app_config,
|
|
chat_model=ANY,
|
|
)
|
|
assert config["context"]["subagent_enabled"] is False
|
|
assert config["configurable"]["subagent_enabled"] is False
|
|
assert config["metadata"]["allowed_subagents"] == []
|
|
assert config["metadata"]["mcp_plugins"] == mcp_plugins
|
|
|
|
|
|
def test_make_lead_agent_no_agent_settings_passes_none_overrides(monkeypatch):
|
|
"""Without a custom agent, model_overrides is None (no behavior change)."""
|
|
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["model_overrides"] = model_overrides
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
lead_agent_module._make_lead_agent({"context": {"model_name": "safe-model"}}, app_config=app_config)
|
|
|
|
assert captured["model_overrides"] is None
|
|
|
|
|
|
def test_internal_make_lead_agent_applies_the_required_thinking_contract(monkeypatch):
|
|
"""A required-thinking model (issue #5073) turns a ``thinking_enabled=False`` request
|
|
back on and maps the generic effort through the contract *before* the factory
|
|
runs, so the assembly metadata and the model see the same effective policy."""
|
|
model = ModelConfig(
|
|
name="glm-5.3-flash",
|
|
display_name="GLM-5.3-Flash",
|
|
description=None,
|
|
use="langchain_openai:ChatOpenAI",
|
|
model="glm-5.3-flash",
|
|
supports_vision=False,
|
|
reasoning={
|
|
"thinking": "required",
|
|
"dialect": "openai_extra_body",
|
|
"effort": {"values": ["low", "high", "max"], "default": "max", "aliases": {"minimal": "low", "medium": "high"}},
|
|
},
|
|
)
|
|
app_config = _make_app_config([model])
|
|
|
|
import deerflow.tools as tools_module
|
|
|
|
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
|
|
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
|
|
monkeypatch.setattr(lead_agent_module, "build_middlewares", lambda config, model_name, agent_name=None, **kwargs: [])
|
|
|
|
captured: dict[str, object] = {}
|
|
|
|
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None, attach_tracing=True, model_overrides=None):
|
|
captured["thinking_enabled"] = thinking_enabled
|
|
captured["reasoning_effort"] = reasoning_effort
|
|
return object()
|
|
|
|
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
|
|
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
|
|
|
|
config: dict = {"configurable": {"model_name": "glm-5.3-flash", "thinking_enabled": False, "reasoning_effort": "minimal"}}
|
|
lead_agent_module._make_lead_agent(config, app_config=app_config)
|
|
|
|
assert captured == {"thinking_enabled": True, "reasoning_effort": "low"}
|
|
assert config["metadata"]["thinking_enabled"] is True
|
|
assert config["metadata"]["reasoning_effort"] == "low"
|