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ThearchyHelios
964eeaa829
在画布上拖拽编排 agent 工作流，并实时看着它执行。 后端 FastAPI + LangGraph：把画布 JSON 编译成 StateGraph，15 种节点 （模型/Agent/多Agent、工具/沙箱、分支/循环/子图、记忆/检索/整形、 人工介入/结构校验）。选 LangGraph 是因为可视化编排真正难的是暂停、 恢复和回放，而 checkpointer + interrupt() 正好把这几件事做在了底层。 前端 React + React Flow：节点元数据驱动属性面板与连接桩，运行时 节点高亮、边流动画、卡片内流式 token 全部由同一份事件流驱动。 关键实现： - Agent 内工具审批用 @task 缓存模型调用，interrupt 重放时不重复计费 - 沙箱保持只读根文件系统，代码经 exec+base64 写入（put_archive 会被 只读容器拒绝），断网、非 root、内存/CPU/超时限额 - 检索为向量与 BM25 混合，本地哈希 embedding 零配置可用 - 分支条件走 AST 白名单求值器；HTTP 工具逐 IP 拦截内网与元数据地址 - Mock provider 让无 API Key 也能跑通完整链路 7 个内置模板全部冒烟通过，含真实模型端到端验证。
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
from __future__ import annotations
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import operator
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from typing import Annotated, Any, TypedDict
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from langchain_core.messages import AnyMessage
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from langgraph.graph import add_messages
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def merge_dict(left: dict[str, Any] | None, right: dict[str, Any] | None) -> dict[str, Any]:
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"""浅合并。并行分支各自写不同的 key 时不会互相覆盖。"""
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out = dict(left or {})
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out.update(right or {})
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return out
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def merge_usage(left: dict[str, Any] | None, right: dict[str, Any] | None) -> dict[str, Any]:
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"""累加 token / 成本，其余字段取新值。"""
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out = dict(left or {})
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for key, value in (right or {}).items():
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if isinstance(value, (int, float)) and isinstance(out.get(key), (int, float)):
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out[key] = out[key] + value
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else:
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out[key] = value
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return out
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class GraphState(TypedDict, total=False):
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"""所有节点共享的状态。
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- messages：对话历史，agent 节点之间靠它传递上下文
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- nodes：node_id -> 该节点的结构化输出，模板里用 {{ nodes.xxx.text }} 引用
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- vars：用户显式写入的变量池
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- output：output 节点收集的最终成果
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"""
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messages: Annotated[list[AnyMessage], add_messages]
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input: dict[str, Any]
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vars: Annotated[dict[str, Any], merge_dict]
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nodes: Annotated[dict[str, Any], merge_dict]
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output: Annotated[dict[str, Any], merge_dict]
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usage: Annotated[dict[str, Any], merge_usage]
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trail: Annotated[list[dict[str, Any]], operator.add]
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# 循环计数器：node_id -> 已迭代次数，防止无限循环
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loops: Annotated[dict[str, Any], merge_dict]
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def template_context(state: GraphState) -> dict[str, Any]:
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"""把状态摊平成模板/表达式可以引用的命名空间。"""
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return {
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"input": state.get("input") or {},
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"nodes": state.get("nodes") or {},
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"vars": state.get("vars") or {},
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"output": state.get("output") or {},
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"loops": state.get("loops") or {},
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"usage": state.get("usage") or {},
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"messages": [
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{"role": getattr(m, "type", "?"), "content": _text_of(m)}
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for m in (state.get("messages") or [])
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],
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"last_message": _text_of((state.get("messages") or [None])[-1]),
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}
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def _text_of(message: Any) -> str:
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"""把 LangChain 的多模态 content 压成纯文本。"""
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if message is None:
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return ""
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content = getattr(message, "content", message)
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts: list[str] = []
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for block in content:
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if isinstance(block, str):
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parts.append(block)
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elif isinstance(block, dict):
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if block.get("type") == "text":
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parts.append(block.get("text", ""))
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elif "text" in block:
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parts.append(str(block["text"]))
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return "".join(parts)
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return str(content)
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def message_text(message: Any) -> str:
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return _text_of(message)
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def thinking_text(message: Any) -> str:
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"""抽出思考块的内容。
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开了 adaptive thinking 的模型（Claude 4.6+）返回的 content 里混着 thinking 块，
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正文提取必须跳过它们，但它本身对"看清楚模型怎么想的"很有价值，所以单独取出来。
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"""
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content = getattr(message, "content", message)
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if not isinstance(content, list):
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return ""
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parts: list[str] = []
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for block in content:
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if isinstance(block, dict) and block.get("type") == "thinking":
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parts.append(str(block.get("thinking") or ""))
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return "".join(parts)
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