steven_li
30ab74ffb2
- 集成MCP连接管理器，支持MCP服务器连接 - 添加多种内置工具：ClarifyTool、CronTool、DelegateTool、ExecuteCodeTool、 PatchFileTool、ProcessTool、SendMessageTool、SpawnTool、TerminalTool、 TodoTool、WebFetchTool、WebSearchTool、WriteFileTool等 - 实现工具注册和装配功能 - 添加技能选择上下文参数 - 支持思考模式控制参数thinking_enabled feat(coordinator): 重构任务执行计划器参数命名 - 将learning_candidate_enabled重命名为allow_candidate_generation - 更新TeamGraphScheduler中的参数传递 - 修改LocalAgentRunner中的相关参数处理 - 更新README文档中的相应描述 refactor(context): 标准化工具调用参数格式 - 添加_json导入用于参数序列化 - 实现_provider_tool_calls方法标准化OpenAI兼容的工具调用载荷 - 修复工具调用中参数非字符串类型的序列化问题 refactor(session): 优化消息历史记录过滤逻辑 - 修改get_messages_as_conversation为基于运行状态过滤消息 - 排除未完成、失败或错误结束的运行记录 - 改进对话历史的可见性控制机制 fix(store): 修复FTS索引重建逻辑 - 添加异常处理防止FTS索引创建失败 - 实现_rebuild_fts_index方法重新构建全文搜索索引 - 优化索引触发器和表的维护流程
289 lines
12 KiB
Python
289 lines
12 KiB
Python
"""Internal Task execution planner for single-agent vs team execution."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
from dataclasses import dataclass, field
|
|
from typing import Any, Literal
|
|
|
|
from beaver.coordinator.models import AgentDescriptor, ExecutionGraph, ExecutionNode
|
|
from beaver.engine.providers import ProviderBundle
|
|
|
|
from .models import TaskRecord, ValidationResult
|
|
from .skill_resolver import SkillResolutionReport, TaskSkillResolver
|
|
|
|
|
|
TaskExecutionMode = Literal["single", "team"]
|
|
|
|
|
|
@dataclass(slots=True)
|
|
class TaskExecutionPlan:
|
|
mode: TaskExecutionMode
|
|
reason: str = ""
|
|
graph: ExecutionGraph | None = None
|
|
final_synthesis_instruction: str = ""
|
|
fallback_error: str | None = None
|
|
skill_resolution_report: list[SkillResolutionReport] = field(default_factory=list)
|
|
|
|
@property
|
|
def is_team(self) -> bool:
|
|
return self.mode == "team" and self.graph is not None
|
|
|
|
@classmethod
|
|
def single(cls, reason: str, *, fallback_error: str | None = None) -> "TaskExecutionPlan":
|
|
return cls(mode="single", reason=reason, fallback_error=fallback_error)
|
|
|
|
def to_event_payload(self) -> dict[str, Any]:
|
|
strategy = self.graph.strategy if self.graph is not None else None
|
|
nodes = self.graph.nodes if self.graph is not None else []
|
|
return {
|
|
"plan_mode": self.mode,
|
|
"reason": self.reason,
|
|
"strategy": strategy,
|
|
"node_ids": [node.node_id for node in nodes],
|
|
"skill_queries": [
|
|
str(node.agent.metadata.get("skill_query") or "")
|
|
for node in nodes
|
|
],
|
|
"selected_skill_names": [
|
|
name
|
|
for node in nodes
|
|
for name in node.inherited_pinned_skills
|
|
],
|
|
"ephemeral_guidance_ids": [
|
|
item.ephemeral_guidance_id
|
|
for item in self.skill_resolution_report
|
|
if item.ephemeral_guidance_id
|
|
],
|
|
"skill_resolution_report": [item.to_dict() for item in self.skill_resolution_report],
|
|
"fallback_error": self.fallback_error,
|
|
}
|
|
|
|
|
|
class TaskExecutionPlanner:
|
|
"""Plan whether a Task attempt should run through a team first."""
|
|
|
|
_MAX_NODES = 6
|
|
_SUPPORTED_STRATEGIES = {"sequence", "parallel", "dag"}
|
|
|
|
def __init__(self, *, task_skill_resolver: TaskSkillResolver | None = None) -> None:
|
|
self.task_skill_resolver = task_skill_resolver
|
|
|
|
async def plan(
|
|
self,
|
|
*,
|
|
task: TaskRecord,
|
|
user_message: str,
|
|
attempt_index: int,
|
|
latest_validation: ValidationResult | None = None,
|
|
provider_bundle: ProviderBundle | None = None,
|
|
) -> TaskExecutionPlan:
|
|
provider = None
|
|
model = None
|
|
if provider_bundle is not None:
|
|
provider = provider_bundle.auxiliary_provider or provider_bundle.main_provider
|
|
runtime = provider_bundle.auxiliary_runtime or provider_bundle.main_runtime
|
|
model = getattr(runtime, "model", None)
|
|
if provider is None:
|
|
return TaskExecutionPlan.single("planner_provider_unavailable")
|
|
try:
|
|
response = await provider.chat(
|
|
messages=[
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
"You choose whether an internal Beaver Task attempt should run as a single "
|
|
"main-agent pass or use a small sub-agent team first. Return only compact JSON."
|
|
),
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": self._prompt(
|
|
task=task,
|
|
user_message=user_message,
|
|
attempt_index=attempt_index,
|
|
latest_validation=latest_validation,
|
|
),
|
|
},
|
|
],
|
|
tools=None,
|
|
model=model,
|
|
max_tokens=4096,
|
|
temperature=0.0,
|
|
)
|
|
plan = self.from_json(response.content or "")
|
|
return await self._resolve_plan(
|
|
plan,
|
|
task=task,
|
|
user_message=user_message,
|
|
attempt_index=attempt_index,
|
|
provider_bundle=provider_bundle,
|
|
)
|
|
except Exception as exc:
|
|
return TaskExecutionPlan.single("planner_failed", fallback_error=str(exc))
|
|
|
|
async def _resolve_plan(
|
|
self,
|
|
plan: TaskExecutionPlan,
|
|
*,
|
|
task: TaskRecord,
|
|
user_message: str,
|
|
attempt_index: int,
|
|
provider_bundle: ProviderBundle | None,
|
|
) -> TaskExecutionPlan:
|
|
if not plan.is_team or self.task_skill_resolver is None:
|
|
return plan
|
|
if provider_bundle is None:
|
|
return TaskExecutionPlan.single("planner_fallback_single", fallback_error="task_skill_resolver_provider_unavailable")
|
|
try:
|
|
assert plan.graph is not None
|
|
graph, reports = await self.task_skill_resolver.resolve_graph(
|
|
plan.graph,
|
|
task=task,
|
|
user_message=user_message,
|
|
attempt_index=attempt_index,
|
|
provider_bundle=provider_bundle,
|
|
)
|
|
graph.validate()
|
|
plan.graph = graph
|
|
plan.skill_resolution_report = reports
|
|
return plan
|
|
except Exception as exc:
|
|
return TaskExecutionPlan.single("planner_fallback_single", fallback_error=f"task_skill_resolver_failed: {exc}")
|
|
|
|
def from_json(self, text: str) -> TaskExecutionPlan:
|
|
try:
|
|
payload = self._parse_json_object(text)
|
|
mode = str(payload.get("mode") or "single").strip().lower()
|
|
reason = str(payload.get("reason") or "")
|
|
if mode != "team":
|
|
return TaskExecutionPlan.single(reason or "planner_selected_single")
|
|
|
|
graph = self._graph_from_payload(payload)
|
|
graph.validate()
|
|
return TaskExecutionPlan(
|
|
mode="team",
|
|
reason=reason or "planner_selected_team",
|
|
graph=graph,
|
|
final_synthesis_instruction=str(payload.get("final_synthesis_instruction") or ""),
|
|
)
|
|
except Exception as exc:
|
|
return TaskExecutionPlan.single("planner_fallback_single", fallback_error=str(exc))
|
|
|
|
def _graph_from_payload(self, payload: dict[str, Any]) -> ExecutionGraph:
|
|
strategy = str(payload.get("strategy") or "sequence").strip().lower()
|
|
if strategy not in self._SUPPORTED_STRATEGIES:
|
|
raise ValueError(f"Unsupported team strategy: {strategy}")
|
|
raw_nodes = payload.get("nodes")
|
|
if not isinstance(raw_nodes, list) or not raw_nodes:
|
|
raise ValueError("Team plan requires at least one node")
|
|
if len(raw_nodes) > self._MAX_NODES:
|
|
raise ValueError(f"Team plan exceeds max node count {self._MAX_NODES}")
|
|
|
|
nodes: list[ExecutionNode] = []
|
|
for index, item in enumerate(raw_nodes, start=1):
|
|
if not isinstance(item, dict):
|
|
raise ValueError("Each team node must be an object")
|
|
agent_payload = item.get("agent") if isinstance(item.get("agent"), dict) else {}
|
|
skill_query = str(item.get("skill_query") or agent_payload.get("skill_query") or item.get("task") or "").strip()
|
|
requested_capabilities = _string_list(
|
|
item.get("required_capabilities") or item.get("capabilities") or agent_payload.get("capabilities")
|
|
)
|
|
requested_tags = _string_list(item.get("tags") or agent_payload.get("tags"))
|
|
node_id = str(item.get("node_id") or item.get("id") or agent_payload.get("name") or f"node_{index}").strip()
|
|
task = str(item.get("task") or "").strip()
|
|
if not node_id or not task:
|
|
raise ValueError("Each team node requires node_id/id and task")
|
|
nodes.append(
|
|
ExecutionNode(
|
|
node_id=node_id,
|
|
task=task,
|
|
agent=AgentDescriptor(
|
|
name=node_id,
|
|
role="",
|
|
system_prompt="",
|
|
metadata={
|
|
"skill_query": skill_query,
|
|
"required_capabilities": requested_capabilities,
|
|
"requested_tags": requested_tags,
|
|
"sub_agent_kind": "generic_skill_worker",
|
|
},
|
|
),
|
|
depends_on=[str(dep) for dep in item.get("depends_on") or []],
|
|
inherited_pinned_skills=[str(name) for name in item.get("pinned_skills") or []],
|
|
constraints=[str(value) for value in item.get("constraints") or []],
|
|
expected_output=str(item.get("expected_output") or "") or None,
|
|
)
|
|
)
|
|
return ExecutionGraph(strategy=strategy, nodes=nodes) # type: ignore[arg-type]
|
|
|
|
@staticmethod
|
|
def _prompt(
|
|
*,
|
|
task: TaskRecord,
|
|
user_message: str,
|
|
attempt_index: int,
|
|
latest_validation: ValidationResult | None,
|
|
) -> str:
|
|
validation_note = ""
|
|
if latest_validation is not None:
|
|
validation_note = (
|
|
"\nPrevious validation issues:\n"
|
|
+ json.dumps(latest_validation.to_dict(), ensure_ascii=False)
|
|
)
|
|
return (
|
|
"Decide execution mode for this internal Task attempt.\n"
|
|
"Use mode=team only when independent research, review, implementation slices, or staged checks "
|
|
"would materially improve the result. Otherwise use mode=single.\n\n"
|
|
"JSON schema:\n"
|
|
"{\n"
|
|
' "mode": "single" | "team",\n'
|
|
' "reason": "short reason",\n'
|
|
' "strategy": "sequence" | "parallel" | "dag",\n'
|
|
' "nodes": [{"node_id": "api_review", "task": "...", "skill_query": "API contract review", '
|
|
'"required_capabilities": ["schema compatibility"], "depends_on": []}],\n'
|
|
' "final_synthesis_instruction": "how the main agent should synthesize team output"\n'
|
|
"}\n\n"
|
|
f"Task goal:\n{task.goal}\n\n"
|
|
f"Current user request:\n{user_message}\n\n"
|
|
f"Attempt index: {attempt_index}\n"
|
|
f"{validation_note}"
|
|
)
|
|
|
|
@staticmethod
|
|
def _parse_json_object(text: str) -> dict[str, Any]:
|
|
cleaned = text.strip()
|
|
if cleaned.startswith("```"):
|
|
cleaned = cleaned.strip("`")
|
|
if cleaned.lower().startswith("json"):
|
|
cleaned = cleaned[4:].strip()
|
|
start = cleaned.find("{")
|
|
end = cleaned.rfind("}")
|
|
if start >= 0 and end >= start:
|
|
cleaned = cleaned[start : end + 1]
|
|
payload = json.loads(cleaned)
|
|
if not isinstance(payload, dict):
|
|
raise ValueError("planner response must be a JSON object")
|
|
return payload
|
|
|
|
|
|
def _optional_str(value: Any) -> str | None:
|
|
if value in (None, ""):
|
|
return None
|
|
text = str(value).strip()
|
|
return text or None
|
|
|
|
|
|
def _string_list(value: Any) -> list[str]:
|
|
if not isinstance(value, list):
|
|
if isinstance(value, str):
|
|
value = [item.strip() for item in value.split(",")]
|
|
else:
|
|
return []
|
|
result: list[str] = []
|
|
for item in value:
|
|
text = str(item).strip()
|
|
if text and text not in result:
|
|
result.append(text)
|
|
return result
|