refactor(agent-runner): migrate deerflow and weknora to plugins

docs/agent-runner-pluginization/OFFICIAL_RUNNER_PLUGINS.md

@@ -31,8 +31,10 @@ langbot-app/
 | `coze-api` | `langbot/coze-agent` | `plugin:langbot/coze-agent/default` |
 | - | `langbot/litellm-agent-platform-agent` | `plugin:langbot/litellm-agent-platform-agent/default` |
 | `dashscope-app-api` | `langbot/dashscope-agent` | `plugin:langbot/dashscope-agent/default` |
+| `deerflow-api` | `langbot/deerflow-agent` | `plugin:langbot/deerflow-agent/default` |
 | `langflow-api` | `langbot/langflow-agent` | `plugin:langbot/langflow-agent/default` |
 | `tbox-app-api` | `langbot/tbox-agent` | `plugin:langbot/tbox-agent/default` |
+| `weknora-api` | `langbot/weknora-agent` | `plugin:langbot/weknora-agent/default` |
 
 每个插件可后续提供多个 runner，但迁移目标的默认 runner 统一叫 `default`。
 
@@ -40,7 +42,7 @@ langbot-app/
 
 - **Batch 1（打通协议）**：`local-agent`（能力最完整基准）、`litellm-agent-platform-agent`（外部 code-agent harness 统一入口）、`dify-agent`（传统 service API runner）。
 - **Batch 2（外部 workflow）**：`n8n-agent`、`langflow-agent`（webhook/workflow 输入输出、timeout、外部 conversation id）。
-- **Batch 3（平台 Agent API）**：`coze-agent`、`dashscope-agent`、`tbox-agent`（平台特有响应格式、引用资料、文件/图片输入）。
+- **Batch 3（平台 Agent API）**：`coze-agent`、`dashscope-agent`、`tbox-agent`、`deerflow-agent`、`weknora-agent`（平台特有响应格式、引用资料、文件/图片输入、外部 thread/session 状态）。
 
 ## 4. 每个官方插件的组件要求
 

docs/agent-runner-pluginization/STATUS.md

@@ -35,7 +35,7 @@
 | --- | --- | --- |
 | `plugin:langbot/local-agent/default` | Unit-pass; UI smoke pending | 2026-06-10 本地 pytest / ruff 通过；WebUI smoke 由人工统一执行。 |
 | `plugin:langbot/litellm-agent-platform-agent/default` | Unit-pass; E2E pending | 通过 runner 仓库单测覆盖 HTTP session、run_id prompt 注入和 LangBot MCP gateway；真实 harness E2E 取决于 LiteLLM Agent Platform 部署和 provider 登录态。 |
-| Dify / n8n / Coze / DashScope / Langflow / Tbox | Unit-pass; credential smoke optional | 2026-06-10 plugin layout / parser tests 通过；真实服务凭据 smoke 非每轮必跑。 |
+| Dify / n8n / Coze / DashScope / Langflow / Tbox / DeerFlow / WeKnora | Unit-pass; credential smoke optional | 2026-06-13 plugin layout / parser tests 通过；真实服务凭据 smoke 非每轮必跑。 |
 
 ## 历史高价值记录
 

src/langbot/pkg/agent/runner/config_migration.py

@@ -11,8 +11,10 @@ LEGACY_RUNNER_ID_MAP: dict[str, str] = {
     'n8n-service-api': 'plugin:langbot/n8n-agent/default',
     'coze-api': 'plugin:langbot/coze-agent/default',
     'dashscope-app-api': 'plugin:langbot/dashscope-agent/default',
+    'deerflow-api': 'plugin:langbot/deerflow-agent/default',
     'langflow-api': 'plugin:langbot/langflow-agent/default',
     'tbox-app-api': 'plugin:langbot/tbox-agent/default',
+    'weknora-api': 'plugin:langbot/weknora-agent/default',
 }
 
 

src/langbot/pkg/provider/runner.py

@@ -1,48 +0,0 @@
-from __future__ import annotations
-
-import abc
-import typing
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from ..core import app
-    import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-    import langbot_plugin.api.entities.builtin.provider.message as provider_message
-
-
-# Legacy runner registry kept for provider runners awaiting plugin migration.
-preregistered_runners: list[typing.Type[RequestRunner]] = []
-# TODO(agent-runner): Remove this compatibility layer after the remaining
-# provider runners are migrated to official AgentRunner plugins.
-
-
-def runner_class(name: str):
-    """注册一个请求运行器"""
-
-    def decorator(cls: typing.Type[RequestRunner]) -> typing.Type[RequestRunner]:
-        cls.name = name
-        preregistered_runners.append(cls)
-        return cls
-
-    return decorator
-
-
-class RequestRunner(abc.ABC):
-    """请求运行器"""
-
-    name: str = None
-
-    ap: app.Application
-
-    pipeline_config: dict
-
-    def __init__(self, ap: app.Application, pipeline_config: dict):
-        self.ap = ap
-        self.pipeline_config = pipeline_config
-
-    @abc.abstractmethod
-    async def run(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """运行请求"""
-        pass

src/langbot/pkg/provider/runners/deerflowapi.py

@@ -1,513 +0,0 @@
-"""DeerFlow LangGraph API Runner
-
-参考 astrbot 的 deerflow_agent_runner 实现，适配 LangBot 的 Runner 接口。
-
-特点：
-- 使用 LangGraph HTTP API 接入 deer-flow 后端
-- 自动管理 thread_id（按 session 隔离）
-- 支持 SSE 流式响应解析
-- 支持 streaming/非流式两种输出
-- 处理 values / messages-tuple / custom 三种事件
-"""
-
-from __future__ import annotations
-
-import asyncio
-import hashlib
-import json
-import typing
-from collections import deque
-from dataclasses import dataclass, field
-
-
-from langbot.pkg.provider import runner
-from langbot.pkg.core import app
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-from langbot.libs.deerflow_api import client, errors, stream_utils
-
-
-# TODO(agent-runner): Keep this legacy RequestRunner implementation until
-# DeerFlow is migrated to an official AgentRunner plugin.
-_MAX_VALUES_HISTORY = 200
-
-
-@dataclass
-class _StreamState:
-    """流式状态跟踪"""
-
-    latest_text: str = ''
-    prev_text_for_streaming: str = ''
-    clarification_text: str = ''
-    task_failures: list[str] = field(default_factory=list)
-    seen_message_ids: set[str] = field(default_factory=set)
-    seen_message_order: deque[str] = field(default_factory=deque)
-    no_id_message_fingerprints: dict[int, str] = field(default_factory=dict)
-    baseline_initialized: bool = False
-    has_values_text: bool = False
-    run_values_messages: list[dict[str, typing.Any]] = field(default_factory=list)
-    timed_out: bool = False
-
-
-@runner.runner_class('deerflow-api')
-class DeerFlowAPIRunner(runner.RequestRunner):
-    """DeerFlow LangGraph API 对话请求器"""
-
-    deerflow_client: client.AsyncDeerFlowClient
-
-    def __init__(self, ap: app.Application, pipeline_config: dict):
-        super().__init__(ap, pipeline_config)
-
-        cfg = self.pipeline_config['ai']['deerflow-api']
-
-        api_base = cfg.get('api-base', '').strip()
-        if not api_base or not api_base.startswith(('http://', 'https://')):
-            raise errors.DeerFlowAPIError(
-                message='DeerFlow API Base URL 格式错误，必须以 http:// 或 https:// 开头',
-            )
-
-        self.api_base = api_base
-        self.api_key = cfg.get('api-key', '')
-        self.auth_header = cfg.get('auth-header', '')
-        self.assistant_id = cfg.get('assistant-id', 'lead_agent')
-        self.model_name = cfg.get('model-name', '')
-        self.thinking_enabled = bool(cfg.get('thinking-enabled', False))
-        self.plan_mode = bool(cfg.get('plan-mode', False))
-        self.subagent_enabled = bool(cfg.get('subagent-enabled', False))
-        self.max_concurrent_subagents = int(cfg.get('max-concurrent-subagents', 3))
-        self.timeout = int(cfg.get('timeout', 300))
-        self.recursion_limit = int(cfg.get('recursion-limit', 1000))
-
-        self.deerflow_client = client.AsyncDeerFlowClient(
-            api_base=self.api_base,
-            api_key=self.api_key,
-            auth_header=self.auth_header,
-        )
-
-    # ------------------------------------------------------------------
-    # 辅助方法
-    # ------------------------------------------------------------------
-
-    def _fingerprint_message(self, message: dict[str, typing.Any]) -> str:
-        try:
-            raw = json.dumps(message, sort_keys=True, ensure_ascii=False, default=str)
-        except (TypeError, ValueError):
-            raw = repr(message)
-        return hashlib.sha1(raw.encode('utf-8', errors='ignore')).hexdigest()
-
-    def _remember_seen_message_id(self, state: _StreamState, msg_id: str) -> None:
-        if not msg_id or msg_id in state.seen_message_ids:
-            return
-        state.seen_message_ids.add(msg_id)
-        state.seen_message_order.append(msg_id)
-        while len(state.seen_message_order) > _MAX_VALUES_HISTORY:
-            dropped = state.seen_message_order.popleft()
-            state.seen_message_ids.discard(dropped)
-
-    def _extract_new_messages_from_values(
-        self,
-        values_messages: list[typing.Any],
-        state: _StreamState,
-    ) -> list[dict[str, typing.Any]]:
-        new_messages: list[dict[str, typing.Any]] = []
-        no_id_indexes_seen: set[int] = set()
-        for idx, msg in enumerate(values_messages):
-            if not isinstance(msg, dict):
-                continue
-            msg_id = stream_utils.get_message_id(msg)
-            if msg_id:
-                if msg_id in state.seen_message_ids:
-                    continue
-                self._remember_seen_message_id(state, msg_id)
-                new_messages.append(msg)
-                continue
-
-            no_id_indexes_seen.add(idx)
-            fp = self._fingerprint_message(msg)
-            if state.no_id_message_fingerprints.get(idx) == fp:
-                continue
-            state.no_id_message_fingerprints[idx] = fp
-            new_messages.append(msg)
-
-        for idx in list(state.no_id_message_fingerprints.keys()):
-            if idx not in no_id_indexes_seen:
-                state.no_id_message_fingerprints.pop(idx, None)
-        return new_messages
-
-    # ------------------------------------------------------------------
-    # 用户输入处理
-    # ------------------------------------------------------------------
-
-    def _build_user_content(
-        self,
-        prompt: str,
-        image_urls: list[str],
-    ) -> typing.Any:
-        """构建 LangGraph 兼容的 user content（支持多模态）"""
-        if not image_urls:
-            return prompt
-
-        content: list[dict[str, typing.Any]] = []
-        if prompt:
-            content.append({'type': 'text', 'text': prompt})
-        for url in image_urls:
-            if not isinstance(url, str):
-                continue
-            url = url.strip()
-            if not url:
-                continue
-            if url.startswith(('http://', 'https://', 'data:')):
-                content.append({'type': 'image_url', 'image_url': {'url': url}})
-        return content if content else prompt
-
-    def _preprocess_user_message(
-        self,
-        query: pipeline_query.Query,
-    ) -> tuple[str, list[str]]:
-        """提取用户消息的纯文本与图片 URL 列表"""
-        plain_text = ''
-        image_urls: list[str] = []
-
-        if isinstance(query.user_message.content, str):
-            plain_text = query.user_message.content
-        elif isinstance(query.user_message.content, list):
-            for ce in query.user_message.content:
-                if ce.type == 'text':
-                    plain_text += ce.text
-                elif ce.type == 'image_base64':
-                    # 转换为 data URI 形式
-                    b64 = getattr(ce, 'image_base64', '')
-                    if b64:
-                        if not b64.startswith('data:'):
-                            b64 = f'data:image/png;base64,{b64}'
-                        image_urls.append(b64)
-                elif ce.type == 'image_url':
-                    url = getattr(ce, 'image_url', '')
-                    if url:
-                        image_urls.append(url)
-
-        return plain_text, image_urls
-
-    # ------------------------------------------------------------------
-    # 请求构造
-    # ------------------------------------------------------------------
-
-    def _build_messages(
-        self,
-        prompt: str,
-        image_urls: list[str],
-        system_prompt: str = '',
-    ) -> list[dict[str, typing.Any]]:
-        messages: list[dict[str, typing.Any]] = []
-        if system_prompt:
-            messages.append({'role': 'system', 'content': system_prompt})
-        messages.append(
-            {
-                'role': 'user',
-                'content': self._build_user_content(prompt, image_urls),
-            }
-        )
-        return messages
-
-    def _build_runtime_configurable(self, thread_id: str) -> dict[str, typing.Any]:
-        cfg: dict[str, typing.Any] = {
-            'thread_id': thread_id,
-            'thinking_enabled': self.thinking_enabled,
-            'is_plan_mode': self.plan_mode,
-            'subagent_enabled': self.subagent_enabled,
-        }
-        if self.subagent_enabled:
-            cfg['max_concurrent_subagents'] = self.max_concurrent_subagents
-        if self.model_name:
-            cfg['model_name'] = self.model_name
-        return cfg
-
-    def _build_payload(
-        self,
-        thread_id: str,
-        prompt: str,
-        image_urls: list[str],
-        system_prompt: str = '',
-    ) -> dict[str, typing.Any]:
-        runtime_configurable = self._build_runtime_configurable(thread_id)
-        return {
-            'assistant_id': self.assistant_id,
-            'input': {
-                'messages': self._build_messages(prompt, image_urls, system_prompt),
-            },
-            'stream_mode': ['values', 'messages-tuple', 'custom'],
-            # DeerFlow 2.0 从 config.configurable 读取运行时覆盖
-            # 同时保留 context 字段做向后兼容
-            'context': dict(runtime_configurable),
-            'config': {
-                'recursion_limit': self.recursion_limit,
-                'configurable': runtime_configurable,
-            },
-        }
-
-    # ------------------------------------------------------------------
-    # Session/Thread 管理
-    # ------------------------------------------------------------------
-
-    async def _ensure_thread_id(self, query: pipeline_query.Query) -> str:
-        """从 query.session 取/创建 deerflow thread_id
-
-        LangBot 使用 `query.session.using_conversation.uuid` 持久化 conversation id，
-        我们复用这个字段存储 deerflow thread_id（与 Dify Runner 同样做法）。
-        """
-        thread_id = query.session.using_conversation.uuid or ''
-        if thread_id:
-            return thread_id
-
-        thread = await self.deerflow_client.create_thread(timeout=min(30, self.timeout))
-        thread_id = thread.get('thread_id', '')
-        if not thread_id:
-            raise errors.DeerFlowAPIError(message=f'DeerFlow create thread 返回数据缺少 thread_id: {thread}')
-
-        query.session.using_conversation.uuid = thread_id
-        return thread_id
-
-    # ------------------------------------------------------------------
-    # 流式事件处理
-    # ------------------------------------------------------------------
-
-    def _handle_values_event(
-        self,
-        data: typing.Any,
-        state: _StreamState,
-    ) -> str | None:
-        """处理 values 事件，返回新的完整文本（增量基础上的全量）"""
-        values_messages = stream_utils.extract_messages_from_values_data(data)
-        if not values_messages:
-            return None
-
-        new_messages: list[dict[str, typing.Any]] = []
-        if not state.baseline_initialized:
-            state.baseline_initialized = True
-            for idx, msg in enumerate(values_messages):
-                if not isinstance(msg, dict):
-                    continue
-                new_messages.append(msg)
-                msg_id = stream_utils.get_message_id(msg)
-                if msg_id:
-                    self._remember_seen_message_id(state, msg_id)
-                    continue
-                state.no_id_message_fingerprints[idx] = self._fingerprint_message(msg)
-        else:
-            new_messages = self._extract_new_messages_from_values(values_messages, state)
-
-        latest_text = ''
-        if new_messages:
-            state.run_values_messages.extend(new_messages)
-            if len(state.run_values_messages) > _MAX_VALUES_HISTORY:
-                state.run_values_messages = state.run_values_messages[-_MAX_VALUES_HISTORY:]
-            latest_text = stream_utils.extract_latest_ai_text(state.run_values_messages)
-            if latest_text:
-                state.has_values_text = True
-            latest_clarification = stream_utils.extract_latest_clarification_text(
-                state.run_values_messages,
-            )
-            if latest_clarification:
-                state.clarification_text = latest_clarification
-
-        return latest_text or None
-
-    def _handle_message_event(
-        self,
-        data: typing.Any,
-        state: _StreamState,
-    ) -> str | None:
-        """处理 messages-tuple 事件，返回增量文本
-
-        当 values 事件已经提供完整文本时，跳过 messages-tuple 的增量
-        """
-        delta = stream_utils.extract_ai_delta_from_event_data(data)
-        if delta and not state.has_values_text:
-            state.latest_text += delta
-            return delta
-
-        maybe_clar = stream_utils.extract_clarification_from_event_data(data)
-        if maybe_clar:
-            state.clarification_text = maybe_clar
-        return None
-
-    def _build_final_text(self, state: _StreamState) -> str:
-        """构建最终输出文本"""
-        if state.clarification_text:
-            return state.clarification_text
-
-        # 优先使用最后一条 AI message 的文本
-        latest_ai = stream_utils.extract_latest_ai_message(state.run_values_messages)
-        if latest_ai:
-            text = stream_utils.extract_text(latest_ai.get('content'))
-            if text:
-                if state.timed_out:
-                    text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
-                return text
-
-        if state.latest_text:
-            text = state.latest_text
-            if state.timed_out:
-                text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
-            return text
-
-        # 提取任务失败信息作兜底
-        failure_text = stream_utils.build_task_failure_summary(state.task_failures)
-        if failure_text:
-            return failure_text
-
-        return 'DeerFlow 返回空响应'
-
-    # ------------------------------------------------------------------
-    # 主流程
-    # ------------------------------------------------------------------
-
-    async def _stream_messages_chunk(
-        self,
-        query: pipeline_query.Query,
-    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        """流式输出生成器"""
-        plain_text, image_urls = self._preprocess_user_message(query)
-
-        system_prompt = ''
-        # LangBot 的 pipeline 通常通过 prompt-preprocess 已注入 system prompt
-        # 这里保持空，让 prompt-preprocess 的内容作为 user message 一并送给 deerflow
-
-        thread_id = await self._ensure_thread_id(query)
-        payload = self._build_payload(
-            thread_id=thread_id,
-            prompt=plain_text or 'continue',
-            image_urls=image_urls,
-            system_prompt=system_prompt,
-        )
-
-        state = _StreamState()
-        prev_text = ''
-        message_idx = 0
-
-        try:
-            async for event in self.deerflow_client.stream_run(
-                thread_id=thread_id,
-                payload=payload,
-                timeout=self.timeout,
-            ):
-                event_type = event.get('event')
-                data = event.get('data')
-
-                if event_type == 'values':
-                    new_full = self._handle_values_event(data, state)
-                    if new_full and new_full != prev_text:
-                        delta = new_full[len(prev_text) :] if new_full.startswith(prev_text) else new_full
-                        prev_text = new_full
-                        if delta:
-                            message_idx += 1
-                            yield provider_message.MessageChunk(
-                                role='assistant',
-                                content=new_full,
-                                is_final=False,
-                            )
-                    continue
-
-                if event_type in {'messages-tuple', 'messages', 'message'}:
-                    delta = self._handle_message_event(data, state)
-                    if delta:
-                        prev_text = state.latest_text
-                        message_idx += 1
-                        yield provider_message.MessageChunk(
-                            role='assistant',
-                            content=prev_text,
-                            is_final=False,
-                        )
-                    continue
-
-                if event_type == 'custom':
-                    state.task_failures.extend(
-                        stream_utils.extract_task_failures_from_custom_event(data),
-                    )
-                    continue
-
-                if event_type == 'error':
-                    raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
-
-                if event_type == 'end':
-                    break
-        except (asyncio.TimeoutError, TimeoutError):
-            self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
-            state.timed_out = True
-
-        # 最终消息
-        final_text = self._build_final_text(state)
-        yield provider_message.MessageChunk(
-            role='assistant',
-            content=final_text,
-            is_final=True,
-        )
-
-    async def _messages(
-        self,
-        query: pipeline_query.Query,
-    ) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """非流式聚合输出"""
-        plain_text, image_urls = self._preprocess_user_message(query)
-
-        thread_id = await self._ensure_thread_id(query)
-        payload = self._build_payload(
-            thread_id=thread_id,
-            prompt=plain_text or 'continue',
-            image_urls=image_urls,
-        )
-
-        state = _StreamState()
-
-        try:
-            async for event in self.deerflow_client.stream_run(
-                thread_id=thread_id,
-                payload=payload,
-                timeout=self.timeout,
-            ):
-                event_type = event.get('event')
-                data = event.get('data')
-
-                if event_type == 'values':
-                    self._handle_values_event(data, state)
-                    continue
-
-                if event_type in {'messages-tuple', 'messages', 'message'}:
-                    self._handle_message_event(data, state)
-                    continue
-
-                if event_type == 'custom':
-                    state.task_failures.extend(
-                        stream_utils.extract_task_failures_from_custom_event(data),
-                    )
-                    continue
-
-                if event_type == 'error':
-                    raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
-
-                if event_type == 'end':
-                    break
-        except (asyncio.TimeoutError, TimeoutError):
-            self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
-            state.timed_out = True
-
-        final_text = self._build_final_text(state)
-        yield provider_message.Message(
-            role='assistant',
-            content=final_text,
-        )
-
-    async def run(
-        self,
-        query: pipeline_query.Query,
-    ) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """主入口：根据 adapter 是否支持流式输出，选择流式或非流式"""
-        if await query.adapter.is_stream_output_supported():
-            msg_idx = 0
-            async for msg in self._stream_messages_chunk(query):
-                msg_idx += 1
-                msg.msg_sequence = msg_idx
-                yield msg
-        else:
-            async for msg in self._messages(query):
-                yield msg

src/langbot/pkg/provider/runners/weknoraapi.py

@@ -1,353 +0,0 @@
-from __future__ import annotations
-
-import typing
-import json
-
-
-from langbot.pkg.provider import runner
-from langbot.pkg.core import app
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-from langbot.libs.weknora_api import client, errors
-
-
-# TODO(agent-runner): Keep this legacy RequestRunner implementation until
-# WeKnora is migrated to an official AgentRunner plugin.
-@runner.runner_class('weknora-api')
-class WeKnoraAPIRunner(runner.RequestRunner):
-    """WeKnora API 对话请求器"""
-
-    weknora_client: client.AsyncWeKnoraClient
-
-    def __init__(self, ap: app.Application, pipeline_config: dict):
-        super().__init__(ap, pipeline_config)
-
-        valid_app_types = ['chat', 'agent']
-        if self.pipeline_config['ai']['weknora-api']['app-type'] not in valid_app_types:
-            raise errors.WeKnoraAPIError(
-                f'不支持的 WeKnora 应用类型: {self.pipeline_config["ai"]["weknora-api"]["app-type"]}'
-            )
-
-        api_key = self.pipeline_config['ai']['weknora-api'].get('api-key', '').strip()
-        if not api_key:
-            raise errors.WeKnoraAPIError(
-                'WeKnora API Key 未配置，请在流水线的 WeKnora API 配置中填入 API Key '
-                '(从 WeKnora 前端 设置 → API Keys 生成)'
-            )
-
-        base_url = self.pipeline_config['ai']['weknora-api'].get('base-url', '').strip()
-        if not base_url:
-            raise errors.WeKnoraAPIError('WeKnora Base URL 未配置，请填入服务器地址，例如 http://localhost:8080/api/v1')
-
-        self.weknora_client = client.AsyncWeKnoraClient(
-            api_key=api_key,
-            base_url=base_url,
-        )
-
-    async def _extract_plain_text(self, query: pipeline_query.Query) -> str:
-        """从用户消息中提取纯文本内容"""
-        plain_text = ''
-        if isinstance(query.user_message.content, str):
-            plain_text = query.user_message.content
-        elif isinstance(query.user_message.content, list):
-            for ce in query.user_message.content:
-                if ce.type == 'text':
-                    plain_text += ce.text
-
-        if not plain_text:
-            plain_text = self.pipeline_config['ai']['weknora-api'].get('base-prompt', '')
-
-        return plain_text
-
-    async def _ensure_session(self, query: pipeline_query.Query) -> str:
-        """确保会话存在，如果不存在则创建"""
-        session_id = query.session.using_conversation.uuid or ''
-
-        if not session_id:
-            user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
-            session_id = await self.weknora_client.create_session(title=f'IM Chat - {user_tag}')
-            query.session.using_conversation.uuid = session_id
-
-        return session_id
-
-    async def _agent_chat_messages(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """调用 Agent 智能对话（非流式聚合输出）"""
-        session_id = await self._ensure_session(query)
-        plain_text = await self._extract_plain_text(query)
-        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
-
-        config = self.pipeline_config['ai']['weknora-api']
-        agent_id = config.get('agent-id', 'builtin-smart-reasoning')
-        knowledge_base_ids = config.get('knowledge-base-ids', [])
-        web_search_enabled = config.get('web-search-enabled', False)
-        timeout = config.get('timeout', 120)
-
-        full_answer = ''
-        chunk = None
-
-        async for chunk in self.weknora_client.agent_chat(
-            session_id=session_id,
-            query=plain_text,
-            user=user_tag,
-            agent_id=agent_id,
-            knowledge_base_ids=knowledge_base_ids,
-            web_search_enabled=web_search_enabled,
-            timeout=timeout,
-        ):
-            self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
-
-            response_type = chunk.get('response_type', '')
-            content = chunk.get('content', '')
-
-            if response_type == 'tool_call':
-                # 工具调用
-                tool_data = chunk.get('data', {})
-                tool_name = tool_data.get('tool_name', '')
-                if tool_name:
-                    yield provider_message.Message(
-                        role='assistant',
-                        tool_calls=[
-                            provider_message.ToolCall(
-                                id=chunk.get('id', ''),
-                                type='function',
-                                function=provider_message.FunctionCall(
-                                    name=tool_name,
-                                    arguments=json.dumps(tool_data.get('arguments', {})),
-                                ),
-                            )
-                        ],
-                    )
-
-            elif response_type == 'answer':
-                if content:
-                    full_answer += content
-
-            elif response_type == 'error':
-                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
-
-        if chunk is None:
-            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
-
-        if full_answer:
-            yield provider_message.Message(
-                role='assistant',
-                content=full_answer,
-            )
-
-    async def _chat_messages(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """调用知识库 RAG 问答（非流式聚合输出）"""
-        session_id = await self._ensure_session(query)
-        plain_text = await self._extract_plain_text(query)
-        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
-
-        config = self.pipeline_config['ai']['weknora-api']
-        agent_id = config.get('agent-id', 'builtin-quick-answer')
-        knowledge_base_ids = config.get('knowledge-base-ids', [])
-        timeout = config.get('timeout', 120)
-
-        full_answer = ''
-        chunk = None
-
-        async for chunk in self.weknora_client.knowledge_chat(
-            session_id=session_id,
-            query=plain_text,
-            user=user_tag,
-            agent_id=agent_id,
-            knowledge_base_ids=knowledge_base_ids,
-            timeout=timeout,
-        ):
-            self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
-
-            response_type = chunk.get('response_type', '')
-            content = chunk.get('content', '')
-
-            if response_type == 'answer':
-                if content:
-                    full_answer += content
-
-            elif response_type == 'error':
-                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
-
-        if chunk is None:
-            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
-
-        if full_answer:
-            yield provider_message.Message(
-                role='assistant',
-                content=full_answer,
-            )
-
-    async def _agent_chat_messages_chunk(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        """调用 Agent 智能对话（流式输出）"""
-        session_id = await self._ensure_session(query)
-        plain_text = await self._extract_plain_text(query)
-        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
-
-        config = self.pipeline_config['ai']['weknora-api']
-        agent_id = config.get('agent-id', 'builtin-smart-reasoning')
-        knowledge_base_ids = config.get('knowledge-base-ids', [])
-        web_search_enabled = config.get('web-search-enabled', False)
-        timeout = config.get('timeout', 120)
-
-        pending_answer = ''
-        message_idx = 0
-        is_final = False
-        chunk = None
-
-        async for chunk in self.weknora_client.agent_chat(
-            session_id=session_id,
-            query=plain_text,
-            user=user_tag,
-            agent_id=agent_id,
-            knowledge_base_ids=knowledge_base_ids,
-            web_search_enabled=web_search_enabled,
-            timeout=timeout,
-        ):
-            self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
-
-            response_type = chunk.get('response_type', '')
-            content = chunk.get('content', '')
-            done = chunk.get('done', False)
-
-            if response_type == 'tool_call':
-                tool_data = chunk.get('data', {})
-                tool_name = tool_data.get('tool_name', '')
-                if tool_name:
-                    message_idx += 1
-                    yield provider_message.MessageChunk(
-                        role='assistant',
-                        tool_calls=[
-                            provider_message.ToolCall(
-                                id=chunk.get('id', ''),
-                                type='function',
-                                function=provider_message.FunctionCall(
-                                    name=tool_name,
-                                    arguments=json.dumps(tool_data.get('arguments', {})),
-                                ),
-                            )
-                        ],
-                    )
-
-            elif response_type == 'answer':
-                message_idx += 1
-                if content:
-                    pending_answer += content
-
-                if done:
-                    is_final = True
-
-                # 每 8 个 chunk 输出一次，或最终输出
-                if message_idx % 8 == 0 or is_final:
-                    yield provider_message.MessageChunk(
-                        role='assistant',
-                        content=pending_answer,
-                        is_final=is_final,
-                    )
-
-            elif response_type == 'error':
-                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
-
-        if chunk is None:
-            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
-
-        # 确保最终消息已发出
-        if not is_final and pending_answer:
-            yield provider_message.MessageChunk(
-                role='assistant',
-                content=pending_answer,
-                is_final=True,
-            )
-
-    async def _chat_messages_chunk(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        """调用知识库 RAG 问答（流式输出）"""
-        session_id = await self._ensure_session(query)
-        plain_text = await self._extract_plain_text(query)
-        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
-
-        config = self.pipeline_config['ai']['weknora-api']
-        agent_id = config.get('agent-id', 'builtin-quick-answer')
-        knowledge_base_ids = config.get('knowledge-base-ids', [])
-        timeout = config.get('timeout', 120)
-
-        pending_answer = ''
-        message_idx = 0
-        is_final = False
-        chunk = None
-
-        async for chunk in self.weknora_client.knowledge_chat(
-            session_id=session_id,
-            query=plain_text,
-            user=user_tag,
-            agent_id=agent_id,
-            knowledge_base_ids=knowledge_base_ids,
-            timeout=timeout,
-        ):
-            self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
-
-            response_type = chunk.get('response_type', '')
-            content = chunk.get('content', '')
-            done = chunk.get('done', False)
-
-            if response_type == 'answer':
-                message_idx += 1
-                if content:
-                    pending_answer += content
-
-                if done:
-                    is_final = True
-
-                if message_idx % 8 == 0 or is_final:
-                    yield provider_message.MessageChunk(
-                        role='assistant',
-                        content=pending_answer,
-                        is_final=is_final,
-                    )
-
-            elif response_type == 'error':
-                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
-
-        if chunk is None:
-            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
-
-        if not is_final and pending_answer:
-            yield provider_message.MessageChunk(
-                role='assistant',
-                content=pending_answer,
-                is_final=True,
-            )
-
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """运行请求"""
-        app_type = self.pipeline_config['ai']['weknora-api']['app-type']
-
-        if await query.adapter.is_stream_output_supported():
-            msg_idx = 0
-            if app_type == 'agent':
-                async for msg in self._agent_chat_messages_chunk(query):
-                    msg_idx += 1
-                    msg.msg_sequence = msg_idx
-                    yield msg
-            elif app_type == 'chat':
-                async for msg in self._chat_messages_chunk(query):
-                    msg_idx += 1
-                    msg.msg_sequence = msg_idx
-                    yield msg
-            else:
-                raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
-        else:
-            if app_type == 'agent':
-                async for msg in self._agent_chat_messages(query):
-                    yield msg
-            elif app_type == 'chat':
-                async for msg in self._chat_messages(query):
-                    yield msg
-            else:
-                raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')

tests/unit_tests/COVERAGE_EXCLUSIONS.md

@@ -18,14 +18,7 @@
 - **测试方式**: 需要 mock HTTP 响应或使用 fake LLM server
 - **状态**: 后续可补充 mock HTTP 测试
 
-### 3. Agent Runner (`provider/runners/`)
+### 3. 向量数据库 (`vector/vdbs/`)
-- **路径**: `src/langbot/pkg/provider/runners/`
-- **模块**: cozeapi, difysvapi, n8nsvapi, langflowapi, dashscopeapi, localagent, tboxapi
-- **排除原因**: 需要真实 Agent 平台（Coze、Dify、n8n 等）的 API 连接
-- **测试方式**: 需要 mock Agent 平台响应
-- **状态**: 后续可补充 mock 测试
-
-### 4. 向量数据库 (`vector/vdbs/`)
 - **路径**: `src/langbot/pkg/vector/vdbs/`
 - **模块**: chroma, milvus, pgvector, qdrant, seekdb
 - **排除原因**: 需要真实向量数据库实例运行
@@ -42,7 +35,7 @@
 # 排除外部适配器后计算覆盖率
 pytest tests/unit_tests/ --cov=langbot.pkg \
   --cov-fail-under=0 \
-  -o "cov_exclude_patterns=platform/sources/*,provider/modelmgr/requesters/*,provider/runners/*,vector/vdbs/*"
+  -o "cov_exclude_patterns=platform/sources/*,provider/modelmgr/requesters/*,vector/vdbs/*"
 ```
 
 ### 当前覆盖率（排除后）
@@ -77,15 +70,11 @@ pytest tests/unit_tests/ --cov=langbot.pkg \
    - 使用 `httpx` mock 测试 API 响应解析
    - 测试重试逻辑、错误处理
 
-2. **`provider/runners/`** (优先级：中)
+2. **`platform/sources/`** (优先级：低)
-   - Mock Agent 平台响应
-   - 测试 session 管理、错误处理
-
-3. **`platform/sources/`** (优先级：低)
    - Mock 平台 webhook 事件
    - 测试消息解析、事件处理
 
-4. **`vector/vdbs/`** (优先级：低)
+3. **`vector/vdbs/`** (优先级：低)
    - Mock 向量数据库操作
    - 测试 CRUD、查询逻辑
 
@@ -176,4 +165,4 @@ tests/unit_tests/
 | `core` | **28%** | 1289 | 🔄 需补充 app 启动 |
 | `persistence` | **24%** | 1099 | 🔄 需补充 mgr |
 
-外部适配器测试需要 mock 环境或集成测试，不属于纯单元测试范畴。
+外部适配器测试需要 mock 环境或集成测试，不属于纯单元测试范畴。

tests/unit_tests/agent/test_config_migration.py

@@ -32,6 +32,32 @@ class TestResolveRunnerId:
         runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
         assert runner_id == 'plugin:langbot/local-agent/default'
 
+    def test_resolves_deerflow_and_weknora_legacy_runner_fields(self):
+        assert (
+            ConfigMigration.resolve_runner_id(
+                {
+                    'ai': {
+                        'runner': {
+                            'runner': 'deerflow-api',
+                        },
+                    },
+                }
+            )
+            == 'plugin:langbot/deerflow-agent/default'
+        )
+        assert (
+            ConfigMigration.resolve_runner_id(
+                {
+                    'ai': {
+                        'runner': {
+                            'runner': 'weknora-api',
+                        },
+                    },
+                }
+            )
+            == 'plugin:langbot/weknora-agent/default'
+        )
+
     def test_resolve_no_runner_config(self):
         runner_id = ConfigMigration.resolve_runner_id({})
         assert runner_id is None
@@ -73,6 +99,38 @@ class TestResolveRunnerConfig:
         )
         assert config == {'model': {'primary': 'uuid-123', 'fallbacks': []}}
 
+    def test_reads_deerflow_and_weknora_legacy_runner_blocks(self):
+        pipeline_config = {
+            'ai': {
+                'deerflow-api': {
+                    'api-base': 'http://127.0.0.1:2026',
+                    'assistant-id': 'lead_agent',
+                },
+                'weknora-api': {
+                    'base-url': 'http://localhost:8080/api/v1',
+                    'app-type': 'agent',
+                },
+            },
+        }
+
+        deerflow_config = ConfigMigration.resolve_runner_config(
+            pipeline_config,
+            'plugin:langbot/deerflow-agent/default',
+        )
+        weknora_config = ConfigMigration.resolve_runner_config(
+            pipeline_config,
+            'plugin:langbot/weknora-agent/default',
+        )
+
+        assert deerflow_config == {
+            'api-base': 'http://127.0.0.1:2026',
+            'assistant-id': 'lead_agent',
+        }
+        assert weknora_config == {
+            'base-url': 'http://localhost:8080/api/v1',
+            'app-type': 'agent',
+        }
+
     def test_resolve_no_config(self):
         config = ConfigMigration.resolve_runner_config(
             {},
@@ -155,3 +213,45 @@ class TestNormalizePipelineConfig:
             'model': {'primary': 'old-model', 'fallbacks': []},
             'knowledge-bases': ['kb_1'],
         }
+
+    def test_migrates_deerflow_legacy_runner_block(self):
+        config = {
+            'ai': {
+                'runner': {'runner': 'deerflow-api'},
+                'deerflow-api': {
+                    'api-base': 'http://127.0.0.1:2026',
+                    'assistant-id': 'lead_agent',
+                },
+            },
+        }
+
+        migrated = ConfigMigration.migrate_pipeline_config(config)
+
+        assert migrated['ai']['runner']['id'] == 'plugin:langbot/deerflow-agent/default'
+        assert 'runner' not in migrated['ai']['runner']
+        assert 'deerflow-api' not in migrated['ai']
+        assert migrated['ai']['runner_config']['plugin:langbot/deerflow-agent/default'] == {
+            'api-base': 'http://127.0.0.1:2026',
+            'assistant-id': 'lead_agent',
+        }
+
+    def test_migrates_weknora_legacy_runner_block(self):
+        config = {
+            'ai': {
+                'runner': {'runner': 'weknora-api'},
+                'weknora-api': {
+                    'base-url': 'http://localhost:8080/api/v1',
+                    'app-type': 'agent',
+                },
+            },
+        }
+
+        migrated = ConfigMigration.migrate_pipeline_config(config)
+
+        assert migrated['ai']['runner']['id'] == 'plugin:langbot/weknora-agent/default'
+        assert 'runner' not in migrated['ai']['runner']
+        assert 'weknora-api' not in migrated['ai']
+        assert migrated['ai']['runner_config']['plugin:langbot/weknora-agent/default'] == {
+            'base-url': 'http://localhost:8080/api/v1',
+            'app-type': 'agent',
+        }