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refactor: thread release config through lead path (#2612)

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Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
This commit is contained in:
greatmengqi
2026-04-28 14:53:18 +08:00
committed by GitHub
parent 69649d8aae
commit e82940c03d
20 changed files with 325 additions and 179 deletions
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backend/packages/harness/deerflow/agents/lead_agent/agent.py
+47 -25
View File
@@ -18,7 +18,7 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
from deerflow.agents.thread_state import ThreadState
from deerflow.config.agents_config import load_agent_config, validate_agent_name
from deerflow.config.app_config import get_app_config
from deerflow.config.app_config import AppConfig, get_app_config
from deerflow.config.memory_config import get_memory_config
from deerflow.config.summarization_config import get_summarization_config
from deerflow.models import create_chat_model
@@ -35,9 +35,9 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
return cfg
def _resolve_model_name(requested_model_name: str | None = None) -> str:
def _resolve_model_name(requested_model_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
app_config = get_app_config()
app_config = app_config or get_app_config()
default_model_name = app_config.models[0].name if app_config.models else None
if default_model_name is None:
raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
@@ -50,7 +50,7 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
return default_model_name
def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
def _create_summarization_middleware(*, app_config: AppConfig | None = None) -> DeerFlowSummarizationMiddleware | None:
"""Create and configure the summarization middleware from config."""
config = get_summarization_config()
@@ -73,9 +73,9 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
# as middleware rather than lead_agent (SummarizationMiddleware is a
# LangChain built-in, so we tag the model at creation time).
if config.model_name:
model = create_chat_model(name=config.model_name, thinking_enabled=False)
model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
else:
model = create_chat_model(thinking_enabled=False)
model = create_chat_model(thinking_enabled=False, app_config=app_config)
model = model.with_config(tags=["middleware:summarize"])
# Prepare kwargs
@@ -99,7 +99,8 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
# the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
# config is not expected to change after startup.
try:
skills_container_path = get_app_config().skills.container_path or "/mnt/skills"
resolved_app_config = app_config or get_app_config()
skills_container_path = resolved_app_config.skills.container_path or "/mnt/skills"
except Exception:
logger.exception("Failed to resolve skills container path; falling back to default")
skills_container_path = "/mnt/skills"
@@ -240,7 +241,14 @@ Being proactive with task management demonstrates thoroughness and ensures all r
# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
# ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
# ClarificationMiddleware should be last to intercept clarification requests after model calls
def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = None):
def _build_middlewares(
config: RunnableConfig,
model_name: str | None,
agent_name: str | None = None,
custom_middlewares: list[AgentMiddleware] | None = None,
*,
app_config: AppConfig | None = None,
):
"""Build middleware chain based on runtime configuration.
Args:
@@ -252,9 +260,10 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
List of middleware instances.
"""
middlewares = build_lead_runtime_middlewares(lazy_init=True)
resolved_app_config = app_config or get_app_config()
# Add summarization middleware if enabled
summarization_middleware = _create_summarization_middleware()
summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
if summarization_middleware is not None:
middlewares.append(summarization_middleware)
@@ -266,7 +275,7 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
middlewares.append(todo_list_middleware)
# Add TokenUsageMiddleware when token_usage tracking is enabled
if get_app_config().token_usage.enabled:
if resolved_app_config.token_usage.enabled:
middlewares.append(TokenUsageMiddleware())
# Add TitleMiddleware
@@ -277,13 +286,12 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
# Add ViewImageMiddleware only if the current model supports vision.
# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
app_config = get_app_config()
model_config = app_config.get_model_config(model_name) if model_name else None
model_config = resolved_app_config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
middlewares.append(ViewImageMiddleware())
# Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
if app_config.tool_search.enabled:
if resolved_app_config.tool_search.enabled:
from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
middlewares.append(DeferredToolFilterMiddleware())
@@ -306,12 +314,13 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
return middlewares
def make_lead_agent(config: RunnableConfig):
def make_lead_agent(config: RunnableConfig, app_config: AppConfig | None = None):
# Lazy import to avoid circular dependency
from deerflow.tools import get_available_tools
from deerflow.tools.builtins import setup_agent
cfg = _get_runtime_config(config)
resolved_app_config = app_config or get_app_config()
thinking_enabled = cfg.get("thinking_enabled", True)
reasoning_effort = cfg.get("reasoning_effort", None)
@@ -327,10 +336,9 @@ def make_lead_agent(config: RunnableConfig):
agent_model_name = agent_config.model if agent_config and agent_config.model else None
# Final model name resolution: request → agent config → global default, with fallback for unknown names
model_name = _resolve_model_name(requested_model_name or agent_model_name)
model_name = _resolve_model_name(requested_model_name or agent_model_name, app_config=resolved_app_config)
app_config = get_app_config()
model_config = app_config.get_model_config(model_name)
model_config = resolved_app_config.get_model_config(model_name)
if model_config is None:
raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
@@ -369,20 +377,34 @@ def make_lead_agent(config: RunnableConfig):
if is_bootstrap:
# Special bootstrap agent with minimal prompt for initial custom agent creation flow
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
middleware=_build_middlewares(config, model_name=model_name),
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent],
middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
system_prompt=apply_prompt_template(
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
available_skills=set(["bootstrap"]),
app_config=resolved_app_config,
),
state_schema=ThreadState,
)
# Default lead agent (unchanged behavior)
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config),
tools=get_available_tools(
model_name=model_name,
groups=agent_config.tool_groups if agent_config else None,
subagent_enabled=subagent_enabled,
app_config=resolved_app_config,
),
middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config),
system_prompt=apply_prompt_template(
subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
agent_name=agent_name,
available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None,
app_config=resolved_app_config,
),
state_schema=ThreadState,
)