enforce workflow data edge order

# Conflicts:
#	pyproject.toml
#	src/langbot/pkg/api/http/controller/main.py
#	src/langbot/pkg/core/app.py
#	src/langbot/pkg/core/stages/build_app.py
#	src/langbot/pkg/provider/modelmgr/requester.py
#	uv.lock
#	web/package-lock.json
#	web/package.json
#	web/pnpm-lock.yaml
#	web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx
#	web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
#	web/src/app/home/components/home-sidebar/HomeSidebar.tsx
#	web/src/app/infra/entities/api/index.ts
#	web/src/app/infra/http/BackendClient.ts
#	web/src/i18n/locales/en-US.ts
#	web/src/i18n/locales/es-ES.ts
#	web/src/i18n/locales/ja-JP.ts
#	web/src/i18n/locales/ru-RU.ts
#	web/src/i18n/locales/th-TH.ts
#	web/src/i18n/locales/vi-VN.ts
#	web/src/i18n/locales/zh-Hans.ts
#	web/src/i18n/locales/zh-Hant.ts
#	web/src/router.tsx

README.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>Production-grade platform for building agentic IM bots.</h3>
 <h4>Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.</h4>
@@ -136,7 +136,7 @@ docker compose --profile all up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot)                             | GPU Platform | ✅     |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758)                                                    | GPU Platform | ✅     |
 | [接口 AI](https://jiekou.ai/)                                                                                     | Gateway      | ✅     |
-| [302.AI](https://share.302.ai/SuTG99)                                                                             | Gateway      | ✅     |
+| [302.AI](https://share.302ai.cn/SuTG99)                                                                             | Gateway      | ✅     |
 | [Qiniu](https://www.qiniu.com/ai/agent)                                                                           | Gateway      | ✅     |
 
 [→ View all integrations](https://link.langbot.app/en/docs/features)

README_CN.md

@@ -136,7 +136,7 @@ docker compose --profile all up -d
 | [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
 | [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | 聚合平台 | ✅ |
 | [小马算力](https://www.tokenpony.cn/453z1) | 聚合平台 | ✅ |
 | [百宝箱Tbox](https://www.tbox.cn/open) | 智能体平台 | ✅ |
 | [七牛云Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |

README_ES.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>Plataforma de grado de producción para construir bots de mensajería instantánea con agentes de IA.</h3>
 <h4>Construya, depure y despliegue bots de IA rápidamente en Slack, Discord, Telegram, WeChat y más.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plataforma GPU | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plataforma GPU | ✅ |
 | [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | Pasarela | ✅ |
 | [Qiniu](https://www.qiniu.com/ai/agent) | Pasarela | ✅ |
 
 [→ Ver todas las integraciones](https://link.langbot.app/en/docs/features)

README_FR.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>Plateforme de niveau production pour construire des bots de messagerie instantanée avec agents IA.</h3>
 <h4>Créez, déboguez et déployez rapidement des bots IA sur Slack, Discord, Telegram, WeChat et plus.</h4>
@@ -132,7 +132,7 @@ docker compose --profile all up -d
 | [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Passerelle | ✅ |
 | [GiteeAI](https://ai.gitee.com/) | Passerelle | ✅ |
 | [接口 AI](https://jiekou.ai/) | Passerelle | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | Passerelle | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | Passerelle | ✅ |
 | [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plateforme GPU | ✅ |
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plateforme GPU | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plateforme GPU | ✅ |

README_JP.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>AIエージェント搭載IMボットを構築するための本番グレードプラットフォーム。</h3>
 <h4>Slack、Discord、Telegram、WeChat などに AI ボットを素早く構築、デバッグ、デプロイ。</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPUプラットフォーム | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPUプラットフォーム | ✅ |
 | [接口 AI](https://jiekou.ai/) | ゲートウェイ | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | ゲートウェイ | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | ゲートウェイ | ✅ |
 | [Qiniu](https://www.qiniu.com/ai/agent) | ゲートウェイ | ✅ |
 
 [→ すべての統合を表示](https://link.langbot.app/en/docs/features)

README_KO.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>AI 에이전트 IM 봇 구축을 위한 프로덕션 등급 플랫폼.</h3>
 <h4>Slack, Discord, Telegram, WeChat 등에 AI 봇을 빠르게 구축, 디버그 및 배포.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 플랫폼 | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 플랫폼 | ✅ |
 | [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | 게이트웨이 | ✅ |
 | [Qiniu](https://www.qiniu.com/ai/agent) | 게이트웨이 | ✅ |
 
 [→ 모든 통합 보기](https://link.langbot.app/en/docs/features)

README_RU.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>Платформа производственного уровня для создания агентных IM-ботов.</h3>
 <h4>Быстро создавайте, отлаживайте и развертывайте ИИ-ботов в Slack, Discord, Telegram, WeChat и других платформах.</h4>
@@ -131,7 +131,7 @@ docker compose --profile all up -d
 | [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Шлюз | ✅ |
 | [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Шлюз | ✅ |
 | [GiteeAI](https://ai.gitee.com/) | Шлюз | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | Шлюз | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | Шлюз | ✅ |
 | [接口 AI](https://jiekou.ai/) | Шлюз | ✅ |
 | [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Платформа GPU | ✅ |
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Платформа GPU | ✅ |

README_TW.md

@@ -137,7 +137,7 @@ docker compose --profile all up -d
 | [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
 | [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | 聚合平台 | ✅ |
 | [Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |
 
 ### TTS（語音合成）

README_VI.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
 
 <h3>Nền tảng cấp sản xuất để xây dựng bot IM với AI agent.</h3>
 <h4>Xây dựng, gỡ lỗi và triển khai bot AI nhanh chóng trên Slack, Discord, Telegram, WeChat và nhiều nền tảng khác.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Nền tảng GPU | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Nền tảng GPU | ✅ |
 | [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
-| [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |
+| [302.AI](https://share.302ai.cn/SuTG99) | Cổng | ✅ |
 | [Qiniu](https://www.qiniu.com/ai/agent) | Cổng | ✅ |
 
 [→ Xem tất cả tích hợp](https://link.langbot.app/en/docs/features)

compare_nodes.py

@@ -0,0 +1,163 @@
+#!/usr/bin/env python3
+"""Compare YAML node definitions with frontend node-configs."""
+
+import yaml
+import os
+import re
+import json
+
+# 1. Parse YAML files
+yaml_dir = 'src/langbot/templates/metadata/nodes'
+yaml_nodes = {}
+
+for filename in sorted(os.listdir(yaml_dir)):
+    if filename.endswith('.yaml'):
+        filepath = os.path.join(yaml_dir, filename)
+        with open(filepath, 'r') as f:
+            data = yaml.safe_load(f)
+            node_name = data.get('name', filename.replace('.yaml', ''))
+            yaml_nodes[node_name] = {
+                'category': data.get('category', ''),
+                'inputs': [i['name'] for i in data.get('inputs', [])],
+                'outputs': [o['name'] for o in data.get('outputs', [])],
+                'config': [c['name'] for c in data.get('config', [])]
+            }
+
+# 2. Parse frontend node-configs TypeScript files
+node_configs_dir = 'web/src/app/home/workflows/components/workflow-editor/node-configs'
+
+frontend_nodes = {}
+
+def parse_ts_file(filepath):
+    """Parse a TypeScript file to extract node configurations."""
+    with open(filepath, 'r') as f:
+        content = f.read()
+    
+    # Find all node type definitions
+    # Pattern: nodeType: 'xxx'
+    node_type_pattern = r"nodeType:\s*'([^']+)'"
+    node_types = re.findall(node_type_pattern, content)
+    
+    # For each node type, extract inputs, outputs, and config
+    for node_type in node_types:
+        # Find the config object for this node type
+        # Look for the section between this nodeType and the next one or end of object
+        pattern = rf"nodeType:\s*'({re.escape(node_type)})'.*?(?=nodeType:|export\s+(const|function)|$)"
+        match = re.search(pattern, content, re.DOTALL)
+        
+        if match:
+            section = match.group(0)
+            
+            # Extract inputs
+            inputs = re.findall(r"createInput\('([^']+)'", section)
+            
+            # Extract outputs  
+            outputs = re.findall(r"createOutput\('([^']+)'", section)
+            
+            # Extract config names
+            config_names = re.findall(r"name:\s*'([^']+)'", section)
+            # Remove duplicates while preserving order
+            seen = set()
+            unique_config = []
+            for c in config_names:
+                if c not in seen:
+                    seen.add(c)
+                    unique_config.append(c)
+            
+            frontend_nodes[node_type] = {
+                'inputs': inputs,
+                'outputs': outputs,
+                'config': unique_config
+            }
+
+# Parse all config files
+for filename in os.listdir(node_configs_dir):
+    if filename.endswith('.ts') and filename != 'types.ts' and filename != 'index.ts':
+        filepath = os.path.join(node_configs_dir, filename)
+        parse_ts_file(filepath)
+
+# 3. Compare and report differences
+print("=" * 80)
+print("WORKFLOW NODE COMPARISON REPORT: YAML vs Frontend")
+print("=" * 80)
+
+all_node_types = sorted(set(list(yaml_nodes.keys()) + list(frontend_nodes.keys())))
+
+discrepancies = []
+
+for node_type in all_node_types:
+    yaml_def = yaml_nodes.get(node_type)
+    frontend_def = frontend_nodes.get(node_type)
+    
+    node_discrepancies = []
+    
+    if not yaml_def:
+        print(f"\n⚠️  {node_type}: ONLY in frontend (not in YAML)")
+        continue
+    if not frontend_def:
+        print(f"\n⚠️  {node_type}: ONLY in YAML (not in frontend)")
+        continue
+    
+    # Compare inputs
+    yaml_inputs = set(yaml_def['inputs'])
+    frontend_inputs = set(frontend_def['inputs'])
+    if yaml_inputs != frontend_inputs:
+        only_yaml = yaml_inputs - frontend_inputs
+        only_frontend = frontend_inputs - yaml_inputs
+        node_discrepancies.append({
+            'type': 'inputs',
+            'only_yaml': list(only_yaml),
+            'only_frontend': list(only_frontend)
+        })
+    
+    # Compare outputs
+    yaml_outputs = set(yaml_def['outputs'])
+    frontend_outputs = set(frontend_def['outputs'])
+    if yaml_outputs != frontend_outputs:
+        only_yaml = yaml_outputs - frontend_outputs
+        only_frontend = frontend_outputs - yaml_outputs
+        node_discrepancies.append({
+            'type': 'outputs',
+            'only_yaml': list(only_yaml),
+            'only_frontend': list(only_frontend)
+        })
+    
+    # Compare config
+    yaml_config = set(yaml_def['config'])
+    frontend_config = set(frontend_def['config'])
+    if yaml_config != frontend_config:
+        only_yaml = yaml_config - frontend_config
+        only_frontend = frontend_config - yaml_config
+        node_discrepancies.append({
+            'type': 'config',
+            'only_yaml': list(only_yaml),
+            'only_frontend': list(only_frontend)
+        })
+    
+    if node_discrepancies:
+        print(f"\n❌ {node_type} ({yaml_def['category']}): HAS DISCREPANCIES")
+        for d in node_discrepancies:
+            print(f"   {d['type']}:")
+            if d['only_yaml']:
+                print(f"     Only in YAML: {d['only_yaml']}")
+            if d['only_frontend']:
+                print(f"     Only in Frontend: {d['only_frontend']}")
+        discrepancies.append((node_type, node_discrepancies))
+    else:
+        print(f"\n✅ {node_type} ({yaml_def['category']}): OK")
+
+print(f"\n{'=' * 80}")
+print(f"SUMMARY: {len(discrepancies)} nodes with discrepancies out of {len(all_node_types)} total")
+print(f"{'=' * 80}")
+
+# Output as JSON for further processing
+output = {
+    'yaml_nodes': {k: v for k, v in yaml_nodes.items()},
+    'frontend_nodes': {k: v for k, v in frontend_nodes.items()},
+    'discrepancies': {k: v for k, v in discrepancies}
+}
+
+with open('node_comparison.json', 'w') as f:
+    json.dump(output, f, indent=2)
+
+print(f"\nDetailed comparison saved to node_comparison.json")

docs/development/workflow-system.md

@@ -0,0 +1,713 @@
+# Workflow 系统开发者文档
+
+本文档面向 LangBot 开发者，详细介绍 Workflow 系统的技术架构、核心组件和扩展方法。
+
+## 目录
+
+- [系统架构概述](#系统架构概述)
+- [目录结构](#目录结构)
+- [核心组件](#核心组件)
+  - [后端模块](#后端模块)
+  - [前端组件](#前端组件)
+- [数据库表结构](#数据库表结构)
+- [API 接口文档](#api-接口文档)
+- [如何添加新节点类型](#如何添加新节点类型)
+- [调试功能实现](#调试功能实现)
+
+---
+
+## 系统架构概述
+
+Workflow 系统采用前后端分离架构，主要包含以下层次：
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                      前端层 (React)                          │
+│  ┌─────────────┬──────────────┬──────────────┬───────────┐  │
+│  │ 可视化编辑器 │   节点面板    │   属性面板   │  调试器   │  │
+│  │ ReactFlow   │  NodePalette │ PropertyPanel│ Debugger  │  │
+│  └─────────────┴──────────────┴──────────────┴───────────┘  │
+├─────────────────────────────────────────────────────────────┤
+│                      API 层 (Quart)                          │
+│  ┌─────────────┬──────────────┬──────────────────────────┐  │
+│  │ Workflow API│  Debug API   │     Node Types API       │  │
+│  └─────────────┴──────────────┴──────────────────────────┘  │
+├─────────────────────────────────────────────────────────────┤
+│                     核心引擎层 (Python)                       │
+│  ┌─────────────┬──────────────┬──────────────┬───────────┐  │
+│  │ Executor    │   Registry   │    Node      │ Entities  │  │
+│  │ 执行引擎     │  节点注册表   │   节点基类   │  数据结构  │  │
+│  └─────────────┴──────────────┴──────────────┴───────────┘  │
+├─────────────────────────────────────────────────────────────┤
+│                      存储层 (SQLAlchemy)                     │
+│  ┌─────────────┬──────────────┬──────────────────────────┐  │
+│  │  Workflow   │  Executions  │        Triggers          │  │
+│  └─────────────┴──────────────┴──────────────────────────┘  │
+└─────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 目录结构
+
+### 后端代码结构
+
+```
+LangBot/src/langbot/pkg/
+├── workflow/                      # Workflow 核心模块
+│   ├── __init__.py               # 模块初始化，导出公共接口
+│   ├── entities.py               # 数据实体定义
+│   ├── executor.py               # 执行引擎
+│   ├── node.py                   # 节点基类和装饰器
+│   ├── registry.py               # 节点类型注册表
+│   └── nodes/                    # 内置节点实现
+│       ├── __init__.py           # 注册所有内置节点
+│       ├── trigger.py            # 触发节点
+│       ├── process.py            # 处理节点
+│       ├── control.py            # 控制节点
+│       └── action.py             # 动作节点
+├── entity/persistence/
+│   └── workflow.py               # 数据库模型
+├── api/http/
+│   ├── controller/groups/workflows/
+│   │   └── workflows.py          # API 路由控制器
+│   └── service/
+│       └── workflow.py           # 业务逻辑服务
+└── persistence/migrations/
+    └── dbm026_workflow_tables.py # 数据库迁移
+```
+
+### 前端代码结构
+
+```
+LangBot/web/src/app/home/workflows/
+├── page.tsx                      # Workflow 列表页
+├── WorkflowDetailContent.tsx     # 详情页内容
+├── store/
+│   └── useWorkflowStore.ts       # Zustand 状态管理
+└── components/
+    ├── workflow-editor/          # 可视化编辑器
+    │   ├── index.ts              # 导出
+    │   ├── WorkflowEditorComponent.tsx  # 主编辑器组件
+    │   ├── WorkflowNodeComponent.tsx    # 自定义节点组件
+    │   ├── NodePalette.tsx       # 节点面板
+    │   ├── PropertyPanel.tsx     # 属性面板
+    │   └── node-configs/         # 节点配置元数据
+    │       ├── types.ts          # 配置类型定义
+    │       ├── trigger-configs.ts
+    │       ├── ai-configs.ts
+    │       ├── process-configs.ts
+    │       ├── control-configs.ts
+    │       ├── action-configs.ts
+    │       ├── integration-configs.ts
+    │       └── index.ts          # 配置汇总
+    ├── workflow-debugger/        # 调试器组件
+    │   ├── index.ts
+    │   └── WorkflowDebugger.tsx
+    ├── workflow-form/            # 表单组件
+    │   └── WorkflowFormComponent.tsx
+    └── workflow-executions/      # 执行历史组件
+        └── WorkflowExecutionsTab.tsx
+```
+
+---
+
+## 核心组件
+
+### 后端模块
+
+#### 1. 执行引擎 (WorkflowExecutor)
+
+位置：[`executor.py`](../../src/langbot/pkg/workflow/executor.py)
+
+执行引擎负责工作流的实际执行，包括：
+
+- **拓扑排序**：确定节点执行顺序
+- **节点执行**：调用各节点的 execute 方法
+- **控制流处理**：处理条件分支、循环、并行执行
+- **错误处理**：支持重试机制
+
+```python
+class WorkflowExecutor:
+    async def execute(
+        self, 
+        workflow: WorkflowDefinition, 
+        context: ExecutionContext,
+        start_node_id: Optional[str] = None
+    ) -> ExecutionContext:
+        """执行工作流"""
+        # 1. 构建执行图
+        # 2. 初始化节点状态
+        # 3. 找到起始节点
+        # 4. 按拓扑顺序执行
+```
+
+**调试执行器 (DebugWorkflowExecutor)**
+
+继承自 WorkflowExecutor，增加了调试支持：
+
+- 断点支持
+- 单步执行
+- 暂停/继续
+- 实时日志
+
+```python
+class DebugWorkflowExecutor(WorkflowExecutor):
+    async def execute_debug(
+        self,
+        workflow: WorkflowDefinition,
+        context: ExecutionContext,
+        debug_state: DebugExecutionState,
+    ) -> ExecutionContext:
+        """调试模式执行"""
+```
+
+#### 2. 节点注册表 (NodeTypeRegistry)
+
+位置：[`registry.py`](../../src/langbot/pkg/workflow/registry.py)
+
+单例模式管理所有节点类型：
+
+```python
+class NodeTypeRegistry:
+    _instance: Optional['NodeTypeRegistry'] = None
+    
+    def register(self, node_type: str, node_class: type[WorkflowNode]):
+        """注册节点类型"""
+        
+    def create_instance(self, node_type: str, node_id: str, config: dict) -> WorkflowNode:
+        """创建节点实例"""
+        
+    def list_all(self) -> list[dict]:
+        """获取所有节点类型的 Schema"""
+```
+
+#### 3. 节点基类 (WorkflowNode)
+
+位置：[`node.py`](../../src/langbot/pkg/workflow/node.py)
+
+所有节点必须继承此基类：
+
+```python
+class WorkflowNode(abc.ABC):
+    # 节点元数据
+    type_name: str = ""
+    name: str = ""
+    description: str = ""
+    category: str = "misc"
+    icon: str = ""
+    
+    # 端口定义
+    inputs: list[NodePort] = []
+    outputs: list[NodePort] = []
+    
+    # 配置 Schema
+    config_schema: list[NodeConfig] = []
+    
+    @abc.abstractmethod
+    async def execute(
+        self, 
+        inputs: dict[str, Any], 
+        context: ExecutionContext
+    ) -> dict[str, Any]:
+        """执行节点逻辑"""
+        pass
+```
+
+#### 4. 数据实体 (entities.py)
+
+主要数据结构：
+
+```python
+class WorkflowDefinition:
+    """工作流定义"""
+    uuid: str
+    name: str
+    nodes: list[NodeDefinition]
+    edges: list[EdgeDefinition]
+    settings: WorkflowSettings
+    
+class ExecutionContext:
+    """执行上下文"""
+    execution_id: str
+    workflow_id: str
+    status: ExecutionStatus
+    variables: dict
+    node_states: dict[str, NodeState]
+    history: list[ExecutionStep]
+```
+
+### 前端组件
+
+#### 1. WorkflowEditorComponent
+
+主编辑器组件，基于 React Flow 实现：
+
+- **画布交互**：拖拽、缩放、平移
+- **节点连接**：自动验证端口类型
+- **撤销/重做**：基于历史记录栈
+- **复制/粘贴**：支持多选复制
+
+关键功能：
+
+```tsx
+function WorkflowEditorInner() {
+  const { nodes, edges, onNodesChange, onEdgesChange, onConnect } = useWorkflowStore();
+  
+  // 拖放添加节点
+  const onDrop = useCallback((event: React.DragEvent) => {
+    const type = event.dataTransfer.getData('application/reactflow');
+    const position = screenToFlowPosition({ x: event.clientX, y: event.clientY });
+    addNode(type, position);
+  }, []);
+  
+  // 复制粘贴
+  const handleCopy = useCallback(() => { ... }, []);
+  const handlePaste = useCallback(() => { ... }, []);
+}
+```
+
+#### 2. NodePalette
+
+节点面板组件，展示可用节点类型：
+
+```tsx
+function NodePalette() {
+  // 按类别组织节点
+  const categories = [
+    { id: 'trigger', name: '触发节点', icon: Zap },
+    { id: 'ai', name: 'AI 节点', icon: Brain },
+    { id: 'process', name: '处理节点', icon: Cpu },
+    { id: 'control', name: '控制节点', icon: GitBranch },
+    { id: 'action', name: '动作节点', icon: Send },
+    { id: 'integration', name: '集成节点', icon: Plug },
+  ];
+  
+  // 拖拽开始
+  const onDragStart = (event: React.DragEvent, nodeType: string) => {
+    event.dataTransfer.setData('application/reactflow', nodeType);
+  };
+}
+```
+
+#### 3. PropertyPanel
+
+属性面板组件，动态渲染节点配置表单：
+
+```tsx
+function PropertyPanel() {
+  const { selectedNodeId, nodes, updateNodeData } = useWorkflowStore();
+  
+  // 根据节点类型获取配置元数据
+  const selectedNode = nodes.find(n => n.id === selectedNodeId);
+  const nodeConfig = getNodeConfig(selectedNode?.data?.nodeType);
+  
+  // 动态渲染配置字段
+  return (
+    <div>
+      {nodeConfig?.fields.map(field => (
+        <ConfigField key={field.name} field={field} />
+      ))}
+    </div>
+  );
+}
+```
+
+#### 4. WorkflowDebugger
+
+调试器组件，支持实时调试：
+
+```tsx
+function WorkflowDebugger({ workflowUuid, workflow }) {
+  const [debugState, setDebugState] = useState<DebugState>('idle');
+  const [executionId, setExecutionId] = useState<string>('');
+  const [logs, setLogs] = useState<ExecutionLog[]>([]);
+  
+  // 启动调试
+  const startDebug = async () => {
+    const result = await backendClient.post(
+      `/api/v1/workflows/${workflowUuid}/debug/start`,
+      { context, variables, breakpoints }
+    );
+    setExecutionId(result.execution_id);
+  };
+  
+  // 轮询状态
+  useEffect(() => {
+    if (debugState === 'running') {
+      const interval = setInterval(fetchState, 500);
+      return () => clearInterval(interval);
+    }
+  }, [debugState]);
+}
+```
+
+#### 5. useWorkflowStore
+
+Zustand 状态管理：
+
+```typescript
+interface WorkflowState {
+  nodes: WorkflowNode[];
+  edges: WorkflowEdge[];
+  selectedNodeId: string | null;
+  history: HistoryEntry[];
+  historyIndex: number;
+  isDirty: boolean;
+  
+  // Actions
+  addNode: (type: string, position: XYPosition) => void;
+  updateNodeData: (nodeId: string, data: Partial<NodeData>) => void;
+  deleteNode: (nodeId: string) => void;
+  undo: () => void;
+  redo: () => void;
+}
+
+export const useWorkflowStore = create<WorkflowState>((set, get) => ({
+  // ... state and actions
+}));
+```
+
+---
+
+## 数据库表结构
+
+### workflows 表
+
+```sql
+CREATE TABLE workflows (
+    uuid VARCHAR(255) PRIMARY KEY,
+    name VARCHAR(255) NOT NULL,
+    description TEXT,
+    emoji VARCHAR(10) DEFAULT '🔄',
+    version INTEGER DEFAULT 1,
+    is_enabled BOOLEAN DEFAULT TRUE,
+    definition JSON NOT NULL,        -- 节点和边定义
+    global_config JSON DEFAULT '{}', -- 全局配置
+    extensions_preferences JSON,     -- 插件和 MCP 配置
+    created_at TIMESTAMP,
+    updated_at TIMESTAMP
+);
+```
+
+### workflow_versions 表
+
+```sql
+CREATE TABLE workflow_versions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    workflow_uuid VARCHAR(255) NOT NULL,
+    version INTEGER NOT NULL,
+    definition JSON NOT NULL,
+    global_config JSON DEFAULT '{}',
+    created_at TIMESTAMP,
+    created_by VARCHAR(255),
+    UNIQUE(workflow_uuid, version)
+);
+```
+
+### workflow_executions 表
+
+```sql
+CREATE TABLE workflow_executions (
+    uuid VARCHAR(255) PRIMARY KEY,
+    workflow_uuid VARCHAR(255) NOT NULL,
+    workflow_version INTEGER NOT NULL,
+    status VARCHAR(20) NOT NULL,  -- pending/running/completed/failed/cancelled
+    trigger_type VARCHAR(50),
+    trigger_data JSON,
+    variables JSON,
+    start_time TIMESTAMP,
+    end_time TIMESTAMP,
+    error TEXT,
+    created_at TIMESTAMP
+);
+```
+
+### workflow_node_executions 表
+
+```sql
+CREATE TABLE workflow_node_executions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    execution_uuid VARCHAR(255) NOT NULL,
+    node_id VARCHAR(100) NOT NULL,
+    node_type VARCHAR(50) NOT NULL,
+    status VARCHAR(20) NOT NULL,
+    inputs JSON,
+    outputs JSON,
+    start_time TIMESTAMP,
+    end_time TIMESTAMP,
+    error TEXT,
+    retry_count INTEGER DEFAULT 0
+);
+```
+
+### workflow_triggers 表
+
+```sql
+CREATE TABLE workflow_triggers (
+    uuid VARCHAR(255) PRIMARY KEY,
+    workflow_uuid VARCHAR(255) NOT NULL,
+    type VARCHAR(50) NOT NULL,  -- message/cron/event/webhook
+    config JSON NOT NULL,
+    is_enabled BOOLEAN DEFAULT TRUE,
+    priority INTEGER DEFAULT 0,
+    created_at TIMESTAMP,
+    updated_at TIMESTAMP
+);
+```
+
+---
+
+## API 接口文档
+
+### Workflow CRUD
+
+| 方法 | 路径 | 描述 |
+|-----|------|------|
+| GET | `/api/v1/workflows` | 获取工作流列表 |
+| POST | `/api/v1/workflows` | 创建工作流 |
+| GET | `/api/v1/workflows/:uuid` | 获取单个工作流 |
+| PUT | `/api/v1/workflows/:uuid` | 更新工作流 |
+| DELETE | `/api/v1/workflows/:uuid` | 删除工作流 |
+| POST | `/api/v1/workflows/:uuid/copy` | 复制工作流 |
+
+### 执行相关
+
+| 方法 | 路径 | 描述 |
+|-----|------|------|
+| POST | `/api/v1/workflows/:uuid/execute` | 手动执行工作流 |
+| GET | `/api/v1/workflows/:uuid/executions` | 获取执行记录 |
+
+### 版本管理
+
+| 方法 | 路径 | 描述 |
+|-----|------|------|
+| GET | `/api/v1/workflows/:uuid/versions` | 获取版本列表 |
+| POST | `/api/v1/workflows/:uuid/rollback/:version` | 回滚到指定版本 |
+
+### 调试 API
+
+| 方法 | 路径 | 描述 |
+|-----|------|------|
+| POST | `/api/v1/workflows/:uuid/debug/start` | 启动调试 |
+| POST | `/api/v1/workflows/:uuid/debug/:exec_id/pause` | 暂停执行 |
+| POST | `/api/v1/workflows/:uuid/debug/:exec_id/resume` | 继续执行 |
+| POST | `/api/v1/workflows/:uuid/debug/:exec_id/stop` | 停止执行 |
+| POST | `/api/v1/workflows/:uuid/debug/:exec_id/step` | 单步执行 |
+| GET | `/api/v1/workflows/:uuid/debug/:exec_id/state` | 获取调试状态 |
+
+### 节点类型
+
+| 方法 | 路径 | 描述 |
+|-----|------|------|
+| GET | `/api/v1/workflows/_/node-types` | 获取所有节点类型 |
+| GET | `/api/v1/workflows/_/node-types/categories` | 按类别获取节点类型 |
+
+---
+
+## 如何添加新节点类型
+
+### 步骤 1：创建节点类
+
+在 `LangBot/src/langbot/pkg/workflow/nodes/` 下创建或修改文件：
+
+```python
+from ..node import WorkflowNode, NodePort, NodeConfig, workflow_node
+from ..entities import ExecutionContext
+
+@workflow_node('my_custom_node')
+class MyCustomNode(WorkflowNode):
+    """自定义节点"""
+    
+    # 元数据
+    type_name = 'my_custom_node'
+    name = '我的自定义节点'
+    description = '这是一个自定义节点'
+    category = 'process'  # trigger/process/control/action/integration
+    icon = '🔧'
+    
+    # 输入端口
+    inputs = [
+        NodePort(name='input', type='string', description='输入数据', required=True),
+    ]
+    
+    # 输出端口
+    outputs = [
+        NodePort(name='output', type='string', description='输出数据'),
+    ]
+    
+    # 配置字段
+    config_schema = [
+        NodeConfig(
+            name='option',
+            type='select',
+            required=True,
+            options=['选项A', '选项B'],
+            description='选择一个选项'
+        ),
+        NodeConfig(
+            name='value',
+            type='string',
+            required=False,
+            default='默认值',
+            description='配置值'
+        ),
+    ]
+    
+    async def execute(
+        self, 
+        inputs: dict[str, Any], 
+        context: ExecutionContext
+    ) -> dict[str, Any]:
+        """执行节点逻辑"""
+        input_data = inputs.get('input', '')
+        option = self.get_config('option')
+        value = self.get_config('value', '')
+        
+        # 处理逻辑
+        result = f"处理: {input_data} with {option} and {value}"
+        
+        return {'output': result}
+```
+
+### 步骤 2：注册节点
+
+在 `LangBot/src/langbot/pkg/workflow/nodes/__init__.py` 中导入：
+
+```python
+from .process import (
+    CodeExecutorNode,
+    HttpRequestNode,
+    DataTransformNode,
+    MyCustomNode,  # 添加新节点
+)
+```
+
+### 步骤 3：添加前端配置
+
+在 `LangBot/web/src/app/home/workflows/components/workflow-editor/node-configs/` 目录下添加配置：
+
+```typescript
+// process-configs.ts
+export const processNodeConfigs: NodeConfigMap = {
+  // ... 其他配置
+  
+  my_custom_node: {
+    type: 'my_custom_node',
+    label: 'workflows.nodes.myCustomNode',
+    description: 'workflows.nodes.myCustomNodeDesc',
+    icon: 'Wrench',
+    category: 'process',
+    fields: [
+      {
+        name: 'option',
+        type: 'select',
+        label: 'workflows.fields.option',
+        required: true,
+        options: [
+          { value: '选项A', label: '选项 A' },
+          { value: '选项B', label: '选项 B' },
+        ],
+      },
+      {
+        name: 'value',
+        type: 'string',
+        label: 'workflows.fields.value',
+        required: false,
+        defaultValue: '默认值',
+      },
+    ],
+  },
+};
+```
+
+### 步骤 4：添加国际化
+
+在 `LangBot/web/src/i18n/locales/` 中添加翻译：
+
+```typescript
+// zh-Hans.ts
+workflows: {
+  nodes: {
+    myCustomNode: '我的自定义节点',
+    myCustomNodeDesc: '这是一个自定义节点',
+  },
+  fields: {
+    option: '选项',
+    value: '值',
+  },
+}
+```
+
+---
+
+## 调试功能实现
+
+### 后端调试状态管理
+
+```python
+class DebugExecutionState:
+    """调试执行状态"""
+    
+    def __init__(self, execution_id: str, breakpoints: list[str] = None):
+        self.execution_id = execution_id
+        self.status: str = 'running'
+        self.is_paused: bool = False
+        self.is_stopped: bool = False
+        self.breakpoints: set[str] = set(breakpoints or [])
+        self.logs: list[ExecutionLog] = []
+        self._pause_event = asyncio.Event()
+    
+    def pause(self):
+        """暂停执行"""
+        self.is_paused = True
+        self._pause_event.clear()
+    
+    def resume(self):
+        """继续执行"""
+        self.is_paused = False
+        self._pause_event.set()
+    
+    async def wait_if_paused(self):
+        """如果暂停则等待"""
+        if self.is_paused:
+            await self._pause_event.wait()
+```
+
+### 前端调试流程
+
+1. **设置断点**：点击节点设置断点
+2. **启动调试**：调用 `/debug/start` 启动调试执行
+3. **轮询状态**：定期调用 `/debug/:id/state` 获取状态
+4. **控制执行**：调用 pause/resume/step/stop 控制执行
+5. **查看日志**：实时显示执行日志和节点状态
+
+```typescript
+// 调试状态轮询
+const fetchDebugState = async () => {
+  const state = await backendClient.get(
+    `/api/v1/workflows/${workflowUuid}/debug/${executionId}/state`
+  );
+  
+  // 更新节点状态
+  setNodeStates(state.node_states);
+  
+  // 追加新日志
+  if (state.new_logs.length > 0) {
+    setLogs(prev => [...prev, ...state.new_logs]);
+  }
+  
+  // 检查完成状态
+  if (state.status === 'completed' || state.status === 'error') {
+    setDebugState('idle');
+  }
+};
+```
+
+---
+
+## 扩展阅读
+
+- [Workflow 功能设计文档](../../../plans/langbot-workflow-design.md)
+- [用户使用指南](../user-guide/workflow-guide.md)
+- [API 认证文档](../API_KEY_AUTH.md)

docs/review/mcp-resources-pr-2215-review.md

@@ -0,0 +1,196 @@
+# MCP Resources PR #2215 Review
+
+> 更新日期: 2026-06-29
+> 分支: `mcp_resources`
+> PR: langbot-app/LangBot#2215
+> 主题: MCP Resources 在 LangBot 中的产品价值、AgentRunner 集成方式与后续架构方向
+
+## 结论
+
+PR #2215 对 LangBot 有明确价值：它补齐了 MCP 协议中 Resources 这一重要能力，让 MCP server 不再只暴露 tools，也可以暴露文档、代码片段、配置、日志、图片等上下文资源。管理端可以发现和预览资源，Agent 也可以通过当前实现按需列出和读取资源。
+
+但当前 AgentRunner 层的接入方式更接近一个可用的第一阶段方案，而不是最终架构。现在 MCP Resources 被包装成两个 synthetic tools：
+
+- `langbot_mcp_list_resources`
+- `langbot_mcp_read_resource`
+
+这让模型可以通过 function calling 主动探索资源，落地成本低，也复用了已有 `ToolManager` / `LocalAgentRunner` 的工具调用链路。不过从 MCP 规范和主流实现来看，Resources 更适合作为一种一等上下文来源，而不是长期隐藏在工具列表里。
+
+建议保留当前 synthetic tools 作为探索能力，同时把后续主线设计调整为：MCP Resources 是 pipeline / conversation / message 级别可选择、可固定、可审计的上下文输入。
+
+## 当前实现判断
+
+当前 AgentRunner 集成路径如下：
+
+```text
+Pipeline 绑定 MCP server
+  -> query.variables['_pipeline_bound_mcp_servers']
+  -> Preproc 为 local-agent 加载工具
+  -> ToolManager.get_all_tools()
+  -> MCPLoader 注入 synthetic resource tools
+  -> LocalAgentRunner 将工具 schema 传给模型
+  -> 模型发起 list/read tool call
+  -> ToolManager.execute_func_call()
+  -> MCPLoader 调 MCP session.list_resources/read_resource
+  -> tool result 回灌给模型
+```
+
+这个路径的优点是：
+
+- 复用现有工具调用机制，改动范围小。
+- Agent 可以按需探索资源，不需要每轮预先读取所有资源。
+- 可以沿用 pipeline 绑定的 MCP server 范围，避免越权读取未绑定 server。
+- 对已有 MCP tools 行为影响较小。
+
+主要问题是：
+
+- Resources 在语义上被降级成 tools，和 MCP 规范里的 resource primitive 不完全一致。
+- 模型必须先理解并主动调用 `list/read`，资源不会自然成为上下文。
+- pipeline 不能配置“默认携带某些资源”或“本轮附加某些资源”。
+- UI 资源 tab 目前是管理端预览能力，和 Agent 上下文选择没有打通。
+- 对 blob、图片、大文件、结构化资源的处理还比较粗糙。
+- 缺少 resource templates、订阅更新、缓存、chunk、token budget、trace 与审计策略。
+
+## 主流项目做法
+
+### MCP 官方规范
+
+MCP Resources 是 server 暴露上下文数据的协议能力。规范没有要求 resources 必须以 tool call 形式给模型使用，而是把如何选择、过滤、读取和纳入上下文交给 Host application。
+
+这意味着比较正统的集成方式是：LangBot 作为 Host，在 pipeline、会话或消息层决定哪些 resources 进入模型上下文。
+
+参考: https://modelcontextprotocol.io/specification/2025-06-18/server/resources
+
+### VS Code Copilot
+
+VS Code 把 MCP Resources 做成 chat context 的一部分。用户可以通过 `Add Context > MCP Resources` 或命令浏览 MCP resources，并把选中的资源附加到一次 chat request。
+
+这是目前最值得 LangBot 参考的产品形态：资源不是模型工具，而是用户和 Host 可控的上下文附件。
+
+参考: https://code.visualstudio.com/docs/agent-customization/mcp-servers
+
+### Anthropic SDK
+
+Anthropic 的 client-side MCP helpers 提供资源读取和转换能力，例如把 MCP resource 转为 Claude message content 或 file。也就是说，应用先读取 resource，再显式放进模型消息。
+
+这同样是 application-owned context injection，而不是把 resource 伪装成模型工具。
+
+参考: https://platform.claude.com/docs/en/agents-and-tools/mcp-connector
+
+### LangChain MCP Adapters
+
+LangChain 把 MCP Resources 更像 data loader / document input 来处理，可以把资源加载成 `Blob`，再进入 LangChain 的文档、检索或上下文处理链路。
+
+这说明 Resources 很适合作为知识源、文档源或上下文源，而不只是即时工具调用。
+
+参考: https://docs.langchain.com/oss/python/langchain/mcp
+
+### OpenAI Agents SDK
+
+OpenAI Agents SDK 主路径仍偏向 MCP tools，但底层 MCP server API 已经有 `list_resources`、`list_resource_templates`、`read_resource` 等能力。当前形态说明 resources 是 client 能力，但并未默认变成 agent-visible tools。
+
+参考: https://openai.github.io/openai-agents-python/mcp/
+
+### Cline
+
+Cline 会拉取 MCP tools、resources、resourceTemplates、prompts，并通过类似 `access_mcp_resource` 的内置访问方式让模型读取资源。这个方向和 LangBot 当前 synthetic tools 比较接近。
+
+这种模式适合让 Agent 自主探索，但更像 Host 自定义的模型访问协议，不应成为唯一集成路径。
+
+参考: https://github.com/cline/cline/blob/main/src/services/mcp/McpHub.ts
+
+## 建议架构方向
+
+### 1. 保留探索型工具
+
+保留当前两个 synthetic tools：
+
+- `langbot_mcp_list_resources`
+- `langbot_mcp_read_resource`
+
+它们适合处理“用户没有显式选择资源，但 Agent 判断需要探索 MCP server 上下文”的场景。后续可以优化工具描述、返回格式、资源大小限制和错误信息。
+
+### 2. 增加一等 Resource Context
+
+新增一个 Host 层资源上下文概念，例如：
+
+```text
+PipelineResourceBinding
+ConversationResourceAttachment
+MessageResourceAttachment
+```
+
+Preproc 或独立的 `ResourceContextProvider` 在模型调用前读取这些资源，按 MIME 类型、大小、token budget 转为模型可消费的上下文。
+
+### 3. 打通 UI 与 Agent 上下文
+
+当前 MCP 详情页的 Resources tab 可以继续作为资源发现和预览入口。建议增加操作：
+
+- 添加到本轮上下文
+- 固定到当前 pipeline
+- 固定到当前 bot / conversation
+- 查看资源读取历史和错误
+
+这样 UI 资源管理能力才能真正影响 Agent 行为。
+
+### 4. 支持 resource templates
+
+MCP resource templates 允许 server 暴露参数化资源，例如：
+
+```text
+repo://{owner}/{repo}/file/{path}
+log://{service}/{date}
+```
+
+LangBot 后续应支持模板发现、参数填写、实例化和绑定。否则只能使用静态 resources，覆盖面会受限。
+
+### 5. 增加资源处理策略
+
+建议补齐：
+
+- 文本资源 token budget 与截断策略。
+- 大文件 chunk 与摘要策略。
+- 图片/blob 的模型能力判断与 fallback。
+- MIME 类型白名单与安全限制。
+- 缓存与过期策略。
+- `resources/listChanged` 或订阅更新。
+- resource read trace，便于审计 Agent 读取了什么上下文。
+
+## 推荐落地顺序
+
+### Phase 1: 完成当前 PR 可用性
+
+- 保留 synthetic tools。
+- 明确文档说明当前 Agent 集成是 tool-mediated。
+- 完善资源工具描述，降低模型误用概率。
+- 给 read/list 增加大小限制和更清晰的 MIME 处理。
+- 前端 Resources tab 与 Tools tab 分离，保持管理端清晰。
+
+### Phase 2: 做 Host-owned context attachments
+
+- 在 pipeline 或 conversation 层新增 resource attachment 配置。
+- Preproc 读取已绑定 resources，注入模型上下文。
+- UI 支持“添加到上下文 / 固定到 pipeline”。
+- 记录每轮实际注入的 resource URI 和 token 消耗。
+
+### Phase 3: 做完整 MCP Resources 能力
+
+- 支持 resource templates。
+- 支持资源订阅更新。
+- 支持 chunk、summary、RAG 化接入。
+- 为 DifyAgentRunner、LocalAgentRunner 等不同 runner 定义统一资源上下文接口。
+
+## 最终建议
+
+PR #2215 可以作为 MCP Resources 的第一阶段实现继续推进。它让 LangBot 快速拥有“资源发现、预览、按需读取”的闭环，也给 Agent 探索资源提供了可运行路径。
+
+但在正式设计上，不建议把 “Resources == Tools” 固化为长期抽象。LangBot 更应该把 MCP Resources 定位为上下文来源，与 tools、prompts、knowledge base 并列：
+
+```text
+Tools      -> Agent 可以执行的动作
+Resources  -> Host/用户/Agent 可以选择的上下文数据
+Prompts    -> 可复用的任务模板
+Knowledge  -> 可检索、可索引的长期知识
+```
+
+这样既尊重 MCP 协议语义，也能让 LangBot 在 Agent 工作流、企业知识接入和多 MCP server 管理上走得更稳。

docs/user-guide/workflow-guide.md

@@ -0,0 +1,425 @@
+# Workflow 用户指南
+
+本文档帮助您了解和使用 LangBot 的 Workflow（工作流）功能，通过可视化方式构建自动化的对话处理流程。
+
+## 目录
+
+- [功能介绍](#功能介绍)
+- [快速入门](#快速入门)
+- [节点类型说明](#节点类型说明)
+- [编辑器使用指南](#编辑器使用指南)
+- [调试功能](#调试功能)
+- [常见问题解答](#常见问题解答)
+
+---
+
+## 功能介绍
+
+### 什么是 Workflow？
+
+Workflow（工作流）是 LangBot 提供的可视化自动化编排系统。通过拖拽节点、连接边的方式，您可以：
+
+- 📝 **构建复杂的对话流程**：使用条件分支、循环等控制节点
+- 🤖 **调用 AI 能力**：集成 LLM、知识库检索、参数提取
+- 🔗 **连接外部服务**：集成 Dify、n8n、Coze 等平台
+- ⚡ **自动化任务执行**：消息触发、定时触发、Webhook 触发
+
+### Workflow vs Pipeline
+
+| 对比项 | Pipeline | Workflow |
+|-------|----------|----------|
+| 配置方式 | 表单配置 | 可视化拖拽 |
+| 流程控制 | 线性执行 | 支持分支、循环、并行 |
+| 适用场景 | 简单对话 | 复杂流程 |
+| 学习曲线 | 低 | 中等 |
+
+---
+
+## 快速入门
+
+### 第一步：创建 Workflow
+
+1. 在侧边栏点击 **Workflow** 进入工作流列表
+2. 点击右上角 **创建工作流** 按钮
+3. 填写基本信息：
+   - **名称**：给工作流起一个描述性的名字
+   - **描述**：可选，说明工作流的用途
+   - **图标**：选择一个 emoji 作为标识
+
+### 第二步：添加节点
+
+进入编辑器后，左侧是节点面板，中间是画布区域，右侧是属性面板。
+
+1. **添加触发节点**：从左侧面板拖拽一个"消息触发"节点到画布
+2. **添加 AI 节点**：拖拽一个"LLM 调用"节点
+3. **添加回复节点**：拖拽一个"回复消息"节点
+
+### 第三步：连接节点
+
+1. 将鼠标悬停在触发节点的输出端口（右侧小圆点）
+2. 按住鼠标拖拽到 LLM 节点的输入端口（左侧小圆点）
+3. 同样方式连接 LLM 节点和回复节点
+
+```
+[消息触发] ──▶ [LLM 调用] ──▶ [回复消息]
+```
+
+### 第四步：配置节点
+
+点击 LLM 调用节点，在右侧属性面板配置：
+
+- **运行方式**：选择"本地 Agent"
+- **系统提示词**：描述 AI 的角色和行为
+- **模型**：选择要使用的 LLM 模型
+
+点击回复消息节点配置：
+
+- **消息内容**：设置为 `{{nodes.llm_call.outputs.response}}`（引用 LLM 输出）
+
+### 第五步：保存并绑定
+
+1. 点击工具栏的 **保存** 按钮
+2. 返回 Bot 配置页面
+3. 在 Bot 的绑定设置中选择 **Workflow**，然后选择刚创建的工作流
+
+恭喜！您已经创建了第一个 Workflow。
+
+---
+
+## 节点类型说明
+
+### 触发节点 (Trigger)
+
+触发节点是工作流的入口，定义何时启动执行。
+
+| 节点 | 说明 | 输出 |
+|-----|------|------|
+| 消息触发 | 收到消息时触发 | message, sender_id, platform |
+| 定时触发 | 按 Cron 表达式定时触发 | timestamp |
+| Webhook 触发 | 收到 HTTP 请求时触发 | request_body, headers |
+| 事件触发 | 系统事件触发 | event_type, event_data |
+
+**消息触发配置示例**：
+
+```yaml
+触发条件:
+  - 关键词匹配: ["帮助", "help"]
+  - 平台: ["wechat", "qq"]
+```
+
+### AI 节点
+
+AI 节点用于调用各种 AI 能力。
+
+| 节点 | 说明 | 典型用途 |
+|-----|------|---------|
+| LLM 调用 | 调用大语言模型 | 生成回复、理解意图 |
+| 问题分类器 | 对用户问题分类 | 路由到不同处理分支 |
+| 参数提取器 | 从文本提取结构化数据 | 提取订单号、日期等 |
+| 知识库检索 | 查询知识库 | RAG 增强回复 |
+
+**LLM 调用配置示例**：
+
+```yaml
+运行方式: 本地 Agent
+模型: gpt-4
+系统提示词: |
+  你是一个友好的客服助手。
+  请根据用户的问题提供帮助。
+温度: 0.7
+最大 Token 数: 2000
+```
+
+### 处理节点 (Process)
+
+处理节点用于数据处理和外部调用。
+
+| 节点 | 说明 | 典型用途 |
+|-----|------|---------|
+| 代码执行 | 执行 Python/JavaScript 代码 | 数据处理、格式转换 |
+| HTTP 请求 | 发送 HTTP 请求 | 调用外部 API |
+| 数据转换 | JSON/模板转换 | 数据格式化 |
+
+**HTTP 请求配置示例**：
+
+```yaml
+URL: https://api.example.com/data
+方法: POST
+请求头:
+  Content-Type: application/json
+  Authorization: Bearer {{variables.api_key}}
+请求体: |
+  {"query": "{{message.content}}"}
+```
+
+### 控制节点 (Control)
+
+控制节点用于流程控制。
+
+| 节点 | 说明 | 用途 |
+|-----|------|------|
+| 条件分支 | 二选一分支 | if-else 逻辑 |
+| 多路分支 | 多选一分支 | switch-case 逻辑 |
+| 循环 | 遍历数组 | 批量处理 |
+| 并行 | 同时执行多分支 | 并发处理 |
+| 等待 | 暂停执行 | 延时处理 |
+| 合并 | 合并多个分支 | 汇总结果 |
+
+**条件分支配置示例**：
+
+```yaml
+条件表达式: "{{nodes.classifier.outputs.category}}" == "complaint"
+真分支: 投诉处理
+假分支: 普通咨询
+```
+
+### 动作节点 (Action)
+
+动作节点执行具体操作。
+
+| 节点 | 说明 | 用途 |
+|-----|------|------|
+| 发送消息 | 主动发送消息 | 通知、推送 |
+| 回复消息 | 回复当前消息 | 对话回复 |
+| 存储数据 | 保存数据到存储 | 持久化 |
+| 调用 Pipeline | 调用现有 Pipeline | 复用现有流程 |
+
+**回复消息配置示例**：
+
+```yaml
+消息内容: |
+  感谢您的咨询！
+  
+  {{nodes.llm_call.outputs.response}}
+  
+  如有其他问题，随时联系我。
+```
+
+### 集成节点 (Integration)
+
+集成节点连接外部平台。
+
+| 节点 | 说明 | 平台 |
+|-----|------|------|
+| Dify 工作流 | 调用 Dify 应用 | Dify |
+| Dify 知识库 | 查询 Dify 知识库 | Dify |
+| n8n 工作流 | 调用 n8n 流程 | n8n |
+| Langflow | 调用 Langflow 流程 | Langflow |
+| Coze Bot | 调用扣子 Bot | Coze |
+
+**Dify 工作流配置示例**：
+
+```yaml
+API 地址: https://api.dify.ai/v1
+API Key: sk-xxxxx
+应用类型: workflow
+同步对话历史: true
+```
+
+---
+
+## 编辑器使用指南
+
+### 画布操作
+
+| 操作 | 方式 |
+|-----|------|
+| 平移画布 | 按住鼠标中键/空格+左键 拖拽 |
+| 缩放画布 | 鼠标滚轮 / 工具栏按钮 |
+| 框选多个节点 | 按住 Shift + 拖拽框选 |
+| 适应视图 | 点击工具栏"适应"按钮 |
+
+### 节点操作
+
+| 操作 | 方式 |
+|-----|------|
+| 添加节点 | 从左侧面板拖拽到画布 |
+| 移动节点 | 点击节点拖拽 |
+| 删除节点 | 选中后按 Delete / 点击工具栏删除 |
+| 复制节点 | 选中后 Ctrl+C / 工具栏复制 |
+| 粘贴节点 | Ctrl+V / 工具栏粘贴 |
+
+### 连接操作
+
+| 操作 | 方式 |
+|-----|------|
+| 创建连接 | 从输出端口拖拽到输入端口 |
+| 删除连接 | 点击连接线后按 Delete |
+| 选中连接 | 点击连接线 |
+
+### 快捷键
+
+| 快捷键 | 功能 |
+|-------|------|
+| Ctrl + Z | 撤销 |
+| Ctrl + Shift + Z | 重做 |
+| Ctrl + C | 复制 |
+| Ctrl + V | 粘贴 |
+| Delete | 删除选中 |
+| Ctrl + S | 保存 |
+
+### 工具栏功能
+
+```
+[撤销] [重做] | [放大] [缩小] [适应] | [复制] [粘贴] [删除] | [保存] [调试]
+```
+
+---
+
+## 调试功能
+
+### 启动调试
+
+1. 点击工具栏的 **调试** 按钮
+2. 在调试面板中配置初始数据：
+   - **输入消息**：模拟用户发送的消息
+   - **会话 ID**：可选，用于测试会话变量
+   - **变量**：设置初始变量值
+
+3. 点击 **开始调试** 按钮
+
+### 调试控制
+
+| 按钮 | 功能 |
+|-----|------|
+| ▶️ 开始/继续 | 开始或继续执行 |
+| ⏸️ 暂停 | 暂停执行 |
+| ⏹️ 停止 | 停止执行 |
+| ⏭️ 单步 | 执行下一个节点 |
+
+### 断点
+
+- **设置断点**：点击节点上的断点图标
+- **断点触发**：执行到断点时自动暂停
+- **查看状态**：在暂停时查看节点的输入输出
+
+### 执行日志
+
+调试面板下方显示实时日志：
+
+```
+[INFO] 2024-01-15 10:30:00 - Starting debug execution
+[INFO] 2024-01-15 10:30:00 - Executing node: message_trigger
+[DEBUG] 2024-01-15 10:30:00 - Node inputs: {"message": "你好"}
+[INFO] 2024-01-15 10:30:01 - Node completed in 50ms
+[INFO] 2024-01-15 10:30:01 - Executing node: llm_call
+...
+```
+
+### 节点状态颜色
+
+| 颜色 | 状态 |
+|-----|------|
+| 灰色 | 待执行 |
+| 蓝色 | 执行中 |
+| 绿色 | 已完成 |
+| 红色 | 失败 |
+| 黄色 | 已跳过 |
+
+---
+
+## 常见问题解答
+
+### Q1：如何在节点间传递数据？
+
+使用表达式语法引用其他节点的输出：
+
+```
+{{nodes.节点ID.outputs.输出名称}}
+```
+
+例如：
+- `{{nodes.llm_call.outputs.response}}` - 引用 LLM 节点的响应
+- `{{nodes.http_request.outputs.body}}` - 引用 HTTP 请求的响应体
+
+### Q2：如何使用变量？
+
+Workflow 支持三种变量类型：
+
+1. **工作流变量**：`{{variables.变量名}}`
+2. **会话变量**：`{{conversation_variables.变量名}}`
+3. **消息上下文**：`{{message.content}}`、`{{message.sender_id}}`
+
+### Q3：条件分支如何写条件表达式？
+
+支持以下运算符：
+
+- 比较：`==`, `!=`, `>`, `<`, `>=`, `<=`
+- 逻辑：`and`, `or`, `not`
+- 包含：`in`
+
+示例：
+```python
+# 字符串比较
+"{{nodes.classifier.outputs.intent}}" == "purchase"
+
+# 数值比较
+{{nodes.extractor.outputs.amount}} > 1000
+
+# 包含检查
+"退款" in "{{message.content}}"
+```
+
+### Q4：如何处理错误？
+
+1. **节点级重试**：在节点配置中设置重试次数
+2. **全局错误处理**：在 Workflow 设置中配置错误处理策略
+3. **条件分支**：使用条件节点检查上一节点的状态
+
+### Q5：如何查看执行历史？
+
+1. 进入 Workflow 详情页
+2. 点击 **执行历史** 标签
+3. 查看每次执行的状态、耗时、输入输出
+
+### Q6：Workflow 可以被多个 Bot 使用吗？
+
+是的。一个 Workflow 可以被多个 Bot 绑定使用，但每个 Bot 只能绑定一个处理单元（Pipeline 或 Workflow）。
+
+### Q7：如何复制现有的 Workflow？
+
+在 Workflow 列表页，点击工作流卡片右上角的菜单，选择"复制"即可创建副本。
+
+### Q8：支持版本回滚吗？
+
+支持。每次保存都会创建新版本。在 Workflow 详情页可以查看版本历史并回滚到指定版本。
+
+---
+
+## 最佳实践
+
+### 1. 合理命名
+
+- 为节点和 Workflow 使用描述性名称
+- 使用统一的命名规范
+
+### 2. 模块化设计
+
+- 将复杂流程拆分为多个小 Workflow
+- 使用"调用 Pipeline"节点复用现有流程
+
+### 3. 错误处理
+
+- 为关键节点设置重试机制
+- 使用条件分支处理异常情况
+- 添加日志记录便于排查问题
+
+### 4. 测试先行
+
+- 使用调试功能充分测试
+- 准备多种测试场景
+- 检查边界情况
+
+### 5. 性能优化
+
+- 避免不必要的节点
+- 使用并行节点提高效率
+- 合理设置超时时间
+
+---
+
+## 更多资源
+
+- [开发者文档](../development/workflow-system.md)
+- [设计文档](../../../plans/langbot-workflow-design.md)
+- [API 文档](../service-api-openapi.json)

pyproject.toml

@@ -70,7 +70,7 @@ dependencies = [
     "chromadb>=1.0.0,<2.0.0",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb==1.1.0.post3",
-    "langbot-plugin==0.4.6",
+    "langbot-plugin @ file:///home/qinjunyan/code/projects/langbot/langbot-plugin-sdk",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "matrix-nio>=0.25.2",

src/langbot/pkg/api/http/controller/groups/pipelines/embed.py

@@ -62,15 +62,23 @@ class EmbedRouterGroup(group.RouterGroup):
         """Resolve *bot_uuid* to ``(runtime_bot, pipeline_uuid)``.
 
         Returns ``(None, None)`` when the bot does not exist, is not a
-        ``web_page_bot``, is disabled, or has no pipeline bound.
+        ``web_page_bot``, is disabled, or has no pipeline/workflow bound.
         """
         for bot in self.ap.platform_mgr.bots:
             if (
                 bot.bot_entity.uuid == bot_uuid
                 and bot.bot_entity.adapter == 'web_page_bot'
                 and bot.bot_entity.enable
-                and bot.bot_entity.use_pipeline_uuid
             ):
+                # Check for workflow binding first
+                binding_type = getattr(bot.bot_entity, 'binding_type', 'pipeline') or 'pipeline'
+                binding_uuid = getattr(bot.bot_entity, 'binding_uuid', None)
+
+                if binding_type == 'workflow' and binding_uuid:
+                    # For workflow binding, return workflow UUID
+                    return bot, binding_uuid
+                elif bot.bot_entity.use_pipeline_uuid:
+                    # For pipeline binding, return pipeline UUID
                     return bot, bot.bot_entity.use_pipeline_uuid
         return None, None
 

src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py

@@ -86,6 +86,10 @@ class PipelinesRouterGroup(group.RouterGroup):
                         'available_plugins': plugins,
                         'bound_mcp_servers': extensions_prefs.get('mcp_servers', []),
                         'available_mcp_servers': mcp_servers,
+                        'bound_mcp_resources': extensions_prefs.get('mcp_resources', []),
+                        'mcp_resource_agent_read_enabled': extensions_prefs.get(
+                            'mcp_resource_agent_read_enabled', True
+                        ),
                         'bound_skills': extensions_prefs.get('skills', []),
                         'available_skills': available_skills,
                     }
@@ -99,6 +103,8 @@ class PipelinesRouterGroup(group.RouterGroup):
                 bound_plugins = json_data.get('bound_plugins', [])
                 bound_mcp_servers = json_data.get('bound_mcp_servers', [])
                 bound_skills = json_data.get('bound_skills', [])
+                bound_mcp_resources = json_data.get('bound_mcp_resources')
+                mcp_resource_agent_read_enabled = json_data.get('mcp_resource_agent_read_enabled')
 
                 await self.ap.pipeline_service.update_pipeline_extensions(
                     pipeline_uuid,
@@ -108,6 +114,8 @@ class PipelinesRouterGroup(group.RouterGroup):
                     enable_all_mcp_servers,
                     bound_skills=bound_skills,
                     enable_all_skills=enable_all_skills,
+                    bound_mcp_resources=bound_mcp_resources,
+                    mcp_resource_agent_read_enabled=mcp_resource_agent_read_enabled,
                 )
 
                 return self.success()

src/langbot/pkg/api/http/controller/groups/platform/bots.py

@@ -18,7 +18,6 @@ class BotsRouterGroup(group.RouterGroup):
         @self.route('/<bot_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
         async def _(bot_uuid: str) -> str:
             if quart.request.method == 'GET':
-                # 返回运行时信息，包括webhook地址等
                 bot = await self.ap.bot_service.get_runtime_bot_info(bot_uuid)
                 if bot is None:
                     return self.http_status(404, -1, 'bot not found')
@@ -37,30 +36,21 @@ class BotsRouterGroup(group.RouterGroup):
             from_index = json_data.get('from_index', -1)
             max_count = json_data.get('max_count', 10)
             logs, total_count = await self.ap.bot_service.list_event_logs(bot_uuid, from_index, max_count)
-            return self.success(
+            return self.success(data={'logs': logs, 'total_count': total_count})
-                data={
-                    'logs': logs,
-                    'total_count': total_count,
-                }
-            )
 
         @self.route('/<bot_uuid>/send_message', methods=['POST'], auth_type=group.AuthType.API_KEY)
         async def _(bot_uuid: str) -> str:
-            """Send message to a specific target via bot"""
             json_data = await quart.request.json
             target_type = json_data.get('target_type')
             target_id = json_data.get('target_id')
             message_chain_data = json_data.get('message_chain')
 
-            # Validate required fields
             if not target_type:
                 return self.http_status(400, -1, 'target_type is required')
             if not target_id:
                 return self.http_status(400, -1, 'target_id is required')
             if not message_chain_data:
                 return self.http_status(400, -1, 'message_chain is required')
-
-            # Validate target_type
             if target_type not in ['person', 'group']:
                 return self.http_status(400, -1, 'target_type must be either "person" or "group"')
 
@@ -72,3 +62,29 @@ class BotsRouterGroup(group.RouterGroup):
 
                 traceback.print_exc()
                 return self.http_status(500, -1, f'Failed to send message: {str(e)}')
+
+        # ============ Bot Admins ============
+
+        @self.route('/<bot_uuid>/admins', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(bot_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                admins = await self.ap.bot_service.get_bot_admins(bot_uuid)
+                return self.success(data={'admins': admins})
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                launcher_type = json_data.get('launcher_type', '').strip()
+                launcher_id = str(json_data.get('launcher_id', '')).strip()
+                if not launcher_type or not launcher_id:
+                    return self.http_status(400, -1, 'launcher_type and launcher_id are required')
+                try:
+                    admin_id = await self.ap.bot_service.add_bot_admin(bot_uuid, launcher_type, launcher_id)
+                    return self.success(data={'id': admin_id})
+                except Exception as e:
+                    return self.http_status(409, -1, str(e))
+
+        @self.route(
+            '/<bot_uuid>/admins/<int:admin_id>', methods=['DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
+        )
+        async def _(bot_uuid: str, admin_id: int) -> str:
+            await self.ap.bot_service.delete_bot_admin(bot_uuid, admin_id)
+            return self.success()

src/langbot/pkg/api/http/controller/groups/resources/mcp.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import quart
 import traceback
+from urllib.parse import unquote
 
 
 from ... import group
@@ -66,3 +67,50 @@ class MCPRouterGroup(group.RouterGroup):
             server_data = await quart.request.json
             task_id = await self.ap.mcp_service.test_mcp_server(server_name=server_name, server_data=server_data)
             return self.success(data={'task_id': task_id})
+
+        @self.route('/servers/<server_name>/resources', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(server_name: str) -> str:
+            """Get resources from an MCP server"""
+            server_name = unquote(server_name)
+            try:
+                resources = await self.ap.mcp_service.get_mcp_server_resources(server_name)
+                templates = await self.ap.mcp_service.get_mcp_server_resource_templates(server_name)
+                runtime_info = await self.ap.mcp_service.get_runtime_info(server_name)
+                return self.success(
+                    data={
+                        'resources': resources,
+                        'resource_templates': templates,
+                        'resource_capabilities': (runtime_info or {}).get('resource_capabilities', {}),
+                    }
+                )
+            except Exception as e:
+                return self.http_status(500, -1, f'Failed to get resources: {str(e)}')
+
+        @self.route('/servers/<server_name>/resource-templates', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(server_name: str) -> str:
+            """Get resource templates from an MCP server"""
+            server_name = unquote(server_name)
+            try:
+                templates = await self.ap.mcp_service.get_mcp_server_resource_templates(server_name)
+                return self.success(data={'resource_templates': templates})
+            except Exception as e:
+                return self.http_status(500, -1, f'Failed to get resource templates: {str(e)}')
+
+        @self.route('/servers/<server_name>/resources/read', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(server_name: str) -> str:
+            """Read a resource from an MCP server"""
+            server_name = unquote(server_name)
+            data = await quart.request.json
+            uri = data.get('uri')
+            if not uri:
+                return self.http_status(400, -1, 'URI is required')
+            try:
+                envelope = await self.ap.mcp_service.read_mcp_server_resource_envelope(
+                    server_name,
+                    uri,
+                    max_bytes=data.get('max_bytes'),
+                    include_blob=bool(data.get('include_blob', False)),
+                )
+                return self.success(data=envelope)
+            except Exception as e:
+                return self.http_status(500, -1, f'Failed to read resource: {str(e)}')

src/langbot/pkg/api/http/controller/groups/resources/tools.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import quart
+
 from ... import group
 
 
@@ -9,25 +11,41 @@ class ToolsRouterGroup(group.RouterGroup):
         @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
         async def _() -> str:
             """获取所有可用工具列表"""
-            tools = await self.ap.tool_mgr.get_all_tools()
+            pipeline_uuid = quart.request.args.get('pipeline_uuid') or quart.request.args.get('pipeline_id')
+            bound_plugins: list[str] | None = None
+            bound_mcp_servers: list[str] | None = None
 
-            tool_list = []
+            if pipeline_uuid:
-            for tool in tools:
+                pipeline = await self.ap.pipeline_service.get_pipeline(pipeline_uuid)
-                tool_list.append(
+                if pipeline is None:
-                    {
+                    return self.http_status(404, -1, 'pipeline not found')
-                        'name': tool.name,
+
-                        'description': tool.description,
+                extensions_prefs = pipeline.get('extensions_preferences', {}) or {}
-                        'human_desc': tool.human_desc,
+                if not extensions_prefs.get('enable_all_plugins', True):
-                        'parameters': tool.parameters,
+                    bound_plugins = [
+                        f'{plugin.get("author", "")}/{plugin.get("name", "")}'
+                        for plugin in extensions_prefs.get('plugins', [])
+                        if isinstance(plugin, dict) and plugin.get('name')
+                    ]
+                if not extensions_prefs.get('enable_all_mcp_servers', True):
+                    bound_mcp_servers = [
+                        server for server in (extensions_prefs.get('mcp_servers', []) or []) if isinstance(server, str)
+                    ]
+
+            return self.success(
+                data={
+                    'tools': await self.ap.tool_mgr.get_tool_catalog(
+                        bound_plugins,
+                        bound_mcp_servers,
+                        include_skill_authoring=True,
+                    )
                 }
             )
 
-            return self.success(data={'tools': tool_list})
-
         @self.route('/<tool_name>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
         async def _(tool_name: str) -> str:
             """获取特定工具详情"""
-            tools = await self.ap.tool_mgr.get_all_tools()
+            tools = await self.ap.tool_mgr.get_all_tools(include_skill_authoring=True)
 
             for tool in tools:
                 if tool.name == tool_name:

src/langbot/pkg/api/http/controller/groups/user.py

@@ -195,6 +195,13 @@ class UserRouterGroup(group.RouterGroup):
         @self.route('/set-password', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
         async def _(user_email: str) -> str:
             """Set password for Space account (first time) or change password"""
+            # Check if modifying login info is allowed
+            allow_modify_login_info = self.ap.instance_config.data.get('system', {}).get(
+                'allow_modify_login_info', True
+            )
+            if not allow_modify_login_info:
+                return self.http_status(403, -1, 'Modifying login info is disabled')
+
             json_data = await quart.request.json
             new_password = json_data.get('new_password')
             current_password = json_data.get('current_password')

src/langbot/pkg/api/http/controller/groups/workflows/__init__.py

@@ -0,0 +1,5 @@
+# Workflow router group
+from .workflows import WorkflowsRouterGroup, ExecutionsRouterGroup
+from .websocket_chat import WorkflowWebSocketChatRouterGroup
+
+__all__ = ['WorkflowsRouterGroup', 'ExecutionsRouterGroup', 'WorkflowWebSocketChatRouterGroup']

src/langbot/pkg/api/http/controller/groups/workflows/websocket_chat.py

@@ -0,0 +1,260 @@
+"""Workflow WebSocket聊天路由 - 支持工作流调试的双向实时通信"""
+
+import asyncio
+import datetime
+import json
+import logging
+
+import quart
+
+from ... import group
+from ......platform.sources.websocket_manager import ws_connection_manager
+
+logger = logging.getLogger(__name__)
+
+
+@group.group_class('workflow_websocket_chat', '/api/v1/workflows/<workflow_uuid>/ws')
+class WorkflowWebSocketChatRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.quart_app.websocket(self.path + '/connect')
+        async def workflow_websocket_connect(workflow_uuid: str):
+            """
+            建立工作流WebSocket连接
+
+            URL参数:
+                - workflow_uuid: 工作流UUID
+                - session_type: 会话类型 (person/group)
+            """
+            try:
+                session_type = quart.websocket.args.get('session_type', 'person')
+                logger.info(
+                    'Workflow WebSocket connect request received',
+                    extra={
+                        'workflow_uuid': workflow_uuid,
+                        'session_type': session_type,
+                        'path': quart.websocket.path,
+                        'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
+                        'remote_addr': getattr(quart.websocket, 'remote_addr', None),
+                        'user_agent': quart.websocket.headers.get('User-Agent', ''),
+                        'host': quart.websocket.headers.get('Host', ''),
+                        'origin': quart.websocket.headers.get('Origin', ''),
+                    },
+                )
+
+                if session_type not in ['person', 'group']:
+                    await quart.websocket.send(
+                        json.dumps({'type': 'error', 'message': 'session_type must be person or group'})
+                    )
+                    return
+
+                websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
+
+                if not websocket_adapter:
+                    logger.warning(
+                        'Workflow WebSocket adapter missing',
+                        extra={
+                            'workflow_uuid': workflow_uuid,
+                            'session_type': session_type,
+                        },
+                    )
+                    await quart.websocket.send(json.dumps({'type': 'error', 'message': 'WebSocket adapter not found'}))
+                    return
+
+                connection = await ws_connection_manager.add_connection(
+                    websocket=quart.websocket._get_current_object(),
+                    pipeline_uuid=workflow_uuid,
+                    session_type=session_type,
+                    metadata={'user_agent': quart.websocket.headers.get('User-Agent', ''), 'is_workflow': True},
+                )
+
+                await quart.websocket.send(
+                    json.dumps(
+                        {
+                            'type': 'connected',
+                            'connection_id': connection.connection_id,
+                            'workflow_uuid': workflow_uuid,
+                            'session_type': session_type,
+                            'timestamp': connection.created_at.isoformat(),
+                        }
+                    )
+                )
+
+                logger.debug(
+                    f'Workflow WebSocket connection established: {connection.connection_id} '
+                    f'(workflow={workflow_uuid}, session_type={session_type})'
+                )
+
+                receive_task = asyncio.create_task(self._handle_receive(connection, websocket_adapter))
+                send_task = asyncio.create_task(self._handle_send(connection))
+
+                try:
+                    await asyncio.gather(receive_task, send_task)
+                except Exception as e:
+                    logger.error(f'Workflow WebSocket task execution error: {e}')
+                finally:
+                    await ws_connection_manager.remove_connection(connection.connection_id)
+                    logger.debug(f'Workflow WebSocket connection cleaned: {connection.connection_id}')
+
+            except Exception as e:
+                logger.error(
+                    'Workflow WebSocket connection error',
+                    exc_info=True,
+                    extra={
+                        'workflow_uuid': workflow_uuid,
+                        'session_type': quart.websocket.args.get('session_type', 'person'),
+                        'path': quart.websocket.path,
+                        'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
+                        'remote_addr': getattr(quart.websocket, 'remote_addr', None),
+                    },
+                )
+                try:
+                    await quart.websocket.send(json.dumps({'type': 'error', 'message': str(e)}))
+                except Exception as send_error:
+                    logger.debug(
+                        'Failed to send error message to workflow websocket client',
+                        exc_info=True,
+                        extra={
+                            'workflow_uuid': workflow_uuid,
+                            'send_error': str(send_error),
+                        },
+                    )
+
+        @self.route('/messages/<session_type>', methods=['GET'])
+        async def get_messages(workflow_uuid: str, session_type: str) -> str:
+            """获取工作流消息历史"""
+            try:
+                if session_type not in ['person', 'group']:
+                    return self.http_status(400, -1, 'session_type must be person or group')
+
+                websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
+
+                if not websocket_adapter:
+                    return self.http_status(404, -1, 'WebSocket adapter not found')
+
+                messages = websocket_adapter.get_websocket_messages(workflow_uuid, session_type)
+
+                return self.success(data={'messages': messages})
+
+            except Exception as e:
+                return self.http_status(500, -1, f'Internal server error: {str(e)}')
+
+        @self.route('/reset/<session_type>', methods=['POST'])
+        async def reset_session(workflow_uuid: str, session_type: str) -> str:
+            """重置工作流会话"""
+            try:
+                if session_type not in ['person', 'group']:
+                    return self.http_status(400, -1, 'session_type must be person or group')
+
+                websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
+
+                if not websocket_adapter:
+                    return self.http_status(404, -1, 'WebSocket adapter not found')
+
+                websocket_adapter.reset_session(workflow_uuid, session_type)
+
+                return self.success(data={'message': 'Session reset successfully'})
+
+            except Exception as e:
+                return self.http_status(500, -1, f'Internal server error: {str(e)}')
+
+        @self.route('/connections', methods=['GET'])
+        async def get_connections(workflow_uuid: str) -> str:
+            """获取当前工作流连接统计"""
+            try:
+                stats = ws_connection_manager.get_stats()
+                connections = await ws_connection_manager.get_connections_by_pipeline(workflow_uuid)
+
+                return self.success(
+                    data={
+                        'stats': stats,
+                        'connections': [
+                            {
+                                'connection_id': conn.connection_id,
+                                'session_type': conn.session_type,
+                                'created_at': conn.created_at.isoformat(),
+                                'last_active': conn.last_active.isoformat(),
+                                'is_active': conn.is_active,
+                            }
+                            for conn in connections
+                        ],
+                    }
+                )
+
+            except Exception as e:
+                return self.http_status(500, -1, f'Internal server error: {str(e)}')
+
+        @self.route('/broadcast', methods=['POST'])
+        async def broadcast_message(workflow_uuid: str) -> str:
+            """向所有工作流连接广播消息"""
+            try:
+                data = await quart.request.get_json()
+                message = data.get('message')
+
+                if not message:
+                    return self.http_status(400, -1, 'message is required')
+
+                broadcast_data = {
+                    'type': 'broadcast',
+                    'message': message,
+                    'timestamp': datetime.datetime.now().isoformat(),
+                }
+
+                await ws_connection_manager.broadcast_to_pipeline(workflow_uuid, broadcast_data)
+
+                return self.success(data={'message': 'Broadcast sent successfully'})
+
+            except Exception as e:
+                return self.http_status(500, -1, f'Internal server error: {str(e)}')
+
+    async def _handle_receive(self, connection, websocket_adapter):
+        """处理接收消息的任务"""
+        try:
+            while connection.is_active:
+                message = await quart.websocket.receive()
+
+                await ws_connection_manager.update_activity(connection.connection_id)
+
+                try:
+                    data = json.loads(message)
+                    message_type = data.get('type', 'message')
+
+                    if message_type == 'ping':
+                        await connection.send_queue.put(
+                            {'type': 'pong', 'timestamp': datetime.datetime.now().isoformat()}
+                        )
+
+                    elif message_type == 'message':
+                        logger.debug(f'收到工作流消息: {data} from {connection.connection_id}')
+                        await websocket_adapter.handle_websocket_message(connection, data)
+
+                    elif message_type == 'disconnect':
+                        logger.debug(f'Client disconnected: {connection.connection_id}')
+                        break
+
+                    else:
+                        logger.warning(f'Unknown message type: {message_type}')
+
+                except json.JSONDecodeError:
+                    logger.error(f'Invalid JSON message: {message}')
+                    await connection.send_queue.put({'type': 'error', 'message': 'Invalid JSON format'})
+
+        except Exception as e:
+            logger.error(f'Receive message error: {e}', exc_info=True)
+        finally:
+            connection.is_active = False
+
+    async def _handle_send(self, connection):
+        """处理发送消息的任务"""
+        try:
+            while connection.is_active:
+                try:
+                    message = await asyncio.wait_for(connection.send_queue.get(), timeout=1.0)
+                    await quart.websocket.send(json.dumps(message))
+
+                except asyncio.TimeoutError:
+                    continue
+
+        except Exception as e:
+            logger.error(f'Send message error: {e}', exc_info=True)
+        finally:
+            connection.is_active = False

src/langbot/pkg/api/http/controller/groups/workflows/workflows.py

@@ -0,0 +1,484 @@
+from __future__ import annotations
+
+import quart
+
+from ... import group
+from ....service.workflow import WorkflowExecutionFailedError
+
+
+@group.group_class('workflows', '/api/v1/workflows')
+class WorkflowsRouterGroup(group.RouterGroup):
+    """Workflow API router group"""
+
+    async def initialize(self) -> None:
+        # Workflow CRUD
+        @self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            if quart.request.method == 'GET':
+                sort_by = quart.request.args.get('sort_by', 'created_at')
+                sort_order = quart.request.args.get('sort_order', 'DESC')
+                enabled_only = quart.request.args.get('enabled_only', 'false').lower() == 'true'
+                return self.success(
+                    data={'workflows': await self.ap.workflow_service.get_workflows(sort_by, sort_order, enabled_only)}
+                )
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                workflow_uuid = await self.ap.workflow_service.create_workflow(json_data)
+                return self.success(data={'uuid': workflow_uuid})
+
+        # Get node types (available nodes for the editor)
+        @self.route('/_/node-types', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            return self.success(
+                data={
+                    'node_types': await self.ap.workflow_service.get_node_types(),
+                    'categories': await self.ap.workflow_service.get_node_types_by_category_meta(),
+                }
+            )
+
+        # Get node types by category
+        @self.route('/_/node-types/categories', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            return self.success(data={'categories': await self.ap.workflow_service.get_node_types_by_category()})
+
+        # Single workflow operations
+        @self.route(
+            '/<workflow_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
+        )
+        async def _(workflow_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
+                if workflow is None:
+                    return self.http_status(404, -1, 'workflow not found')
+                return self.success(data={'workflow': workflow})
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+                try:
+                    await self.ap.workflow_service.update_workflow(workflow_uuid, json_data)
+                    return self.success()
+                except ValueError as e:
+                    return self.http_status(404, -1, str(e))
+            elif quart.request.method == 'DELETE':
+                await self.ap.workflow_service.delete_workflow(workflow_uuid)
+                return self.success()
+            return self.http_status(405, -1, 'method not allowed')
+
+        # Publish workflow (enable)
+        @self.route('/<workflow_uuid>/publish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.publish_workflow(workflow_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Unpublish workflow (disable)
+        @self.route('/<workflow_uuid>/unpublish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.unpublish_workflow(workflow_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Copy workflow
+        @self.route('/<workflow_uuid>/copy', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            try:
+                new_uuid = await self.ap.workflow_service.copy_workflow(workflow_uuid)
+                return self.success(data={'uuid': new_uuid})
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Execute workflow manually
+        @self.route('/<workflow_uuid>/execute', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            json_data = await quart.request.json or {}
+            trigger_data = json_data.get('trigger_data', {})
+            session_id = json_data.get('session_id')
+            user_id = json_data.get('user_id')
+            bot_id = json_data.get('bot_id')
+
+            try:
+                execution_id = await self.ap.workflow_service.execute_workflow(
+                    workflow_uuid,
+                    trigger_type='manual',
+                    trigger_data=trigger_data,
+                    session_id=session_id,
+                    user_id=user_id,
+                    bot_id=bot_id,
+                )
+                return self.success(data={'execution_id': execution_id})
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+            except WorkflowExecutionFailedError as e:
+                return self.http_status(500, -1, e.message)
+
+        # Get workflow executions
+        @self.route('/<workflow_uuid>/executions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            limit = int(quart.request.args.get('limit', 50))
+            offset = int(quart.request.args.get('offset', 0))
+            executions = await self.ap.workflow_service.get_executions(
+                workflow_uuid=workflow_uuid, limit=limit, offset=offset
+            )
+            return self.success(data=executions)
+
+        @self.route(
+            '/<workflow_uuid>/executions/<execution_uuid>',
+            methods=['GET'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            execution = await self.ap.workflow_service.get_execution(execution_uuid)
+            if execution is None:
+                return self.http_status(404, -1, 'execution not found')
+            if execution.get('workflow_uuid') != workflow_uuid:
+                return self.http_status(404, -1, 'execution not found in workflow')
+            return self.success(data={'execution': execution})
+
+        # Get workflow versions
+        @self.route('/<workflow_uuid>/versions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            versions = await self.ap.workflow_service.get_versions(workflow_uuid)
+            return self.success(data={'versions': versions})
+
+        # Rollback to a specific version
+        @self.route(
+            '/<workflow_uuid>/rollback/<int:version>', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
+        )
+        async def _(workflow_uuid: str, version: int) -> str:
+            try:
+                await self.ap.workflow_service.rollback_to_version(workflow_uuid, version)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Workflow extensions (plugins and MCP servers)
+        @self.route(
+            '/<workflow_uuid>/extensions', methods=['GET', 'PUT'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
+        )
+        async def _(workflow_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
+                if workflow is None:
+                    return self.http_status(404, -1, 'workflow not found')
+
+                # Get available plugins and MCP servers
+                pipeline_component_kinds = ['Command', 'EventListener', 'Tool']
+                plugins = await self.ap.plugin_connector.list_plugins(component_kinds=pipeline_component_kinds)
+                mcp_servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True)
+
+                extensions_prefs = workflow.get('extensions_preferences', {})
+                return self.success(
+                    data={
+                        'enable_all_plugins': extensions_prefs.get('enable_all_plugins', True),
+                        'enable_all_mcp_servers': extensions_prefs.get('enable_all_mcp_servers', True),
+                        'bound_plugins': extensions_prefs.get('plugins', []),
+                        'available_plugins': plugins,
+                        'bound_mcp_servers': extensions_prefs.get('mcp_servers', []),
+                        'available_mcp_servers': mcp_servers,
+                    }
+                )
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+                enable_all_plugins = json_data.get('enable_all_plugins', True)
+                enable_all_mcp_servers = json_data.get('enable_all_mcp_servers', True)
+                bound_plugins = json_data.get('bound_plugins', [])
+                bound_mcp_servers = json_data.get('bound_mcp_servers', [])
+
+                try:
+                    await self.ap.workflow_service.update_workflow_extensions(
+                        workflow_uuid, bound_plugins, bound_mcp_servers, enable_all_plugins, enable_all_mcp_servers
+                    )
+                    return self.success()
+                except ValueError as e:
+                    return self.http_status(404, -1, str(e))
+            return self.http_status(405, -1, 'method not allowed')
+
+        # Debug API - Start debug execution
+        @self.route('/<workflow_uuid>/debug/start', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            json_data = await quart.request.json or {}
+            context = json_data.get('context', {})
+            variables = json_data.get('variables', {})
+            breakpoints = json_data.get('breakpoints', [])
+
+            try:
+                execution_id = await self.ap.workflow_service.start_debug_execution(
+                    workflow_uuid, context=context, variables=variables, breakpoints=breakpoints
+                )
+                return self.success(data={'execution_id': execution_id})
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Debug API - Pause execution
+        @self.route(
+            '/<workflow_uuid>/debug/<execution_uuid>/pause',
+            methods=['POST'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.pause_debug_execution(workflow_uuid, execution_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Debug API - Resume execution
+        @self.route(
+            '/<workflow_uuid>/debug/<execution_uuid>/resume',
+            methods=['POST'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.resume_debug_execution(workflow_uuid, execution_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Debug API - Step execution
+        @self.route(
+            '/<workflow_uuid>/debug/<execution_uuid>/step',
+            methods=['POST'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                result = await self.ap.workflow_service.step_debug_execution(workflow_uuid, execution_uuid)
+                return self.success(data=result)
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Debug API - Stop execution
+        @self.route(
+            '/<workflow_uuid>/debug/<execution_uuid>/stop',
+            methods=['POST'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.stop_debug_execution(workflow_uuid, execution_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Debug API - Get debug state
+        @self.route(
+            '/<workflow_uuid>/debug/<execution_uuid>/state',
+            methods=['GET'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                state = await self.ap.workflow_service.get_debug_state(workflow_uuid, execution_uuid)
+                return self.success(data=state)
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Get execution logs
+        @self.route(
+            '/<workflow_uuid>/executions/<execution_uuid>/logs',
+            methods=['GET'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            limit = int(quart.request.args.get('limit', 100))
+            offset = int(quart.request.args.get('offset', 0))
+            try:
+                result = await self.ap.workflow_service.get_execution_logs(workflow_uuid, execution_uuid, limit, offset)
+                return self.success(data=result)
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Rerun execution
+        @self.route(
+            '/<workflow_uuid>/executions/<execution_uuid>/rerun',
+            methods=['POST'],
+            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
+        )
+        async def _(workflow_uuid: str, execution_uuid: str) -> str:
+            try:
+                new_execution_id = await self.ap.workflow_service.rerun_execution(workflow_uuid, execution_uuid)
+                return self.success(data={'execution_uuid': new_execution_id})
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # Get workflow statistics
+        @self.route('/<workflow_uuid>/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(workflow_uuid: str) -> str:
+            try:
+                stats = await self.ap.workflow_service.get_workflow_stats(workflow_uuid)
+                return self.success(data=stats)
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+
+        # LLM Node Performance Test Endpoint
+        # Tests each step of LLM node execution with detailed timing
+        @self.route('/_/test/llm-node', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            """Test LLM node performance with detailed step-by-step timing.
+            
+            Request body:
+            {
+                "model_uuid": "uuid-of-model",
+                "system_prompt": "optional system prompt",
+                "user_prompt": "test message",
+                "temperature": 0.7,
+                "max_tokens": 100
+            }
+            
+            Response includes timing for each step:
+            - model_fetch: Time to get model from model_mgr
+            - prompt_build: Time to build messages
+            - llm_call: Time for actual LLM invocation
+            - total: Total time
+            - usage: Token usage information
+            """
+            import time
+            
+            json_data = await quart.request.json
+            if not json_data:
+                return self.http_status(400, -1, 'Request body is required')
+            
+            model_uuid = json_data.get('model_uuid', '')
+            if not model_uuid:
+                return self.http_status(400, -1, 'model_uuid is required')
+            
+            user_prompt = json_data.get('user_prompt', 'test')
+            system_prompt = json_data.get('system_prompt', '')
+            temperature = json_data.get('temperature')
+            max_tokens = json_data.get('max_tokens', 0)
+            
+            timings = {}
+            errors = []
+            
+            # Step 1: Model fetch
+            t_start = time.perf_counter()
+            try:
+                runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
+                timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['model_found'] = True
+                timings['model_name'] = runtime_model.model_entity.name if runtime_model else None
+            except Exception as e:
+                timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['model_found'] = False
+                errors.append(f'Model fetch failed: {str(e)}')
+                return self.http_status(400, -1, {
+                    'error': errors[0],
+                    'timings': timings,
+                })
+            
+            # Step 2: Build messages
+            t_start = time.perf_counter()
+            import langbot_plugin.api.entities.builtin.provider.message as provider_message
+            messages = []
+            if system_prompt:
+                messages.append(provider_message.Message(role='system', content=system_prompt))
+            messages.append(provider_message.Message(role='user', content=user_prompt))
+            timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+            
+            # Step 3: Build extra args
+            extra_args = {}
+            if temperature is not None:
+                extra_args['temperature'] = float(temperature)
+            if max_tokens and int(max_tokens) > 0:
+                extra_args['max_tokens'] = int(max_tokens)
+            
+            # Step 4: LLM call
+            t_start = time.perf_counter()
+            try:
+                result_message = await runtime_model.provider.invoke_llm(
+                    query=None,
+                    model=runtime_model,
+                    messages=messages,
+                    funcs=None,
+                    extra_args=extra_args,
+                )
+                timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['llm_call_success'] = True
+                
+                # Extract response text
+                response_text = ''
+                if isinstance(result_message.content, str):
+                    response_text = result_message.content
+                elif isinstance(result_message.content, list):
+                    for elem in result_message.content:
+                        if hasattr(elem, 'text') and elem.text:
+                            response_text += elem.text
+                        elif isinstance(elem, str):
+                            response_text += elem
+                
+                timings['response_length'] = len(response_text)
+                timings['response_preview'] = response_text[:200]
+                
+                # Extract usage
+                usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
+                if hasattr(result_message, 'usage') and result_message.usage:
+                    u = result_message.usage
+                    usage = {
+                        'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
+                        'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
+                        'total_tokens': getattr(u, 'total_tokens', 0) or 0,
+                    }
+                timings['usage'] = usage
+                
+            except Exception as e:
+                timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['llm_call_success'] = False
+                errors.append(f'LLM call failed: {str(e)}')
+            
+            # Calculate total
+            timings['total_ms'] = round(sum([
+                timings.get('model_fetch_ms', 0),
+                timings.get('prompt_build_ms', 0),
+                timings.get('llm_call_ms', 0),
+            ]), 2)
+            
+            # Add breakdown percentage
+            if timings['total_ms'] > 0:
+                timings['breakdown'] = {
+                    'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
+                    'prompt_build_pct': round(timings.get('prompt_build_ms', 0) / timings['total_ms'] * 100, 1),
+                    'llm_call_pct': round(timings.get('llm_call_ms', 0) / timings['total_ms'] * 100, 1),
+                }
+            
+            if errors:
+                timings['errors'] = errors
+            
+            return self.success(data={'test_result': timings})
+
+
+@group.group_class('executions', '/api/v1/executions')
+class ExecutionsRouterGroup(group.RouterGroup):
+    """Workflow execution API router group"""
+
+    async def initialize(self) -> None:
+        # Get all executions (across all workflows)
+        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            limit = int(quart.request.args.get('limit', 50))
+            offset = int(quart.request.args.get('offset', 0))
+            status = quart.request.args.get('status')
+            executions = await self.ap.workflow_service.get_executions(limit=limit, offset=offset, status=status)
+            return self.success(data=executions)
+
+        # Get single execution
+        @self.route('/<execution_uuid>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(execution_uuid: str) -> str:
+            execution = await self.ap.workflow_service.get_execution(execution_uuid)
+            if execution is None:
+                return self.http_status(404, -1, 'execution not found')
+            return self.success(data={'execution': execution})
+
+        # Cancel execution
+        @self.route('/<execution_uuid>/cancel', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(execution_uuid: str) -> str:
+            try:
+                await self.ap.workflow_service.cancel_execution(execution_uuid)
+                return self.success()
+            except ValueError as e:
+                return self.http_status(404, -1, str(e))
+            except RuntimeError as e:
+                return self.http_status(400, -1, str(e))

src/langbot/pkg/api/http/controller/main.py

@@ -17,6 +17,7 @@ from .groups import platform as groups_platform
 from .groups import pipelines as groups_pipelines
 from .groups import knowledge as groups_knowledge
 from .groups import resources as groups_resources
+from .groups import workflows as groups_workflows
 from ...mcp.mount import MCPMount
 
 importutil.import_modules_in_pkg(groups)
@@ -25,6 +26,7 @@ importutil.import_modules_in_pkg(groups_platform)
 importutil.import_modules_in_pkg(groups_pipelines)
 importutil.import_modules_in_pkg(groups_knowledge)
 importutil.import_modules_in_pkg(groups_resources)
+importutil.import_modules_in_pkg(groups_workflows)
 
 
 class HTTPController:

src/langbot/pkg/api/http/service/bot.py

@@ -99,16 +99,23 @@ class BotService:
         # TODO: 检查配置信息格式
         bot_data['uuid'] = str(uuid.uuid4())
 
-        # bind the most recently updated pipeline if any exist
+        # Set default binding_type if not provided
+        if 'binding_type' not in bot_data:
+            bot_data['binding_type'] = 'pipeline'
+
+        # checkout the default pipeline (for backward compatibility)
         result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_pipeline.LegacyPipeline)
+            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
-            .order_by(persistence_pipeline.LegacyPipeline.updated_at.desc())
+                persistence_pipeline.LegacyPipeline.is_default == True
-            .limit(1)
+            )
         )
         pipeline = result.first()
         if pipeline is not None:
             bot_data['use_pipeline_uuid'] = pipeline.uuid
             bot_data['use_pipeline_name'] = pipeline.name
+            # Also set binding_uuid for new unified binding model
+            if 'binding_uuid' not in bot_data:
+                bot_data['binding_uuid'] = pipeline.uuid
 
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_bot.Bot).values(bot_data))
 
@@ -120,26 +127,45 @@ class BotService:
 
     async def update_bot(self, bot_uuid: str, bot_data: dict) -> None:
         """Update bot"""
-        update_data = bot_data.copy()
+        if 'uuid' in bot_data:
+            del bot_data['uuid']
 
-        if 'uuid' in update_data:
+        # Handle binding_type and binding_uuid for the new unified binding model
-            del update_data['uuid']
+        # If binding_type is explicitly set to 'workflow', skip pipeline validation
+        binding_type = bot_data.get('binding_type')
 
-        # set use_pipeline_name
+        # set use_pipeline_name (for backward compatibility with 'pipeline' binding_type)
-        if 'use_pipeline_uuid' in update_data:
+        # Only validate pipeline when binding_type is 'pipeline' or not set (default to pipeline)
+        if 'use_pipeline_uuid' in bot_data and binding_type != 'workflow':
             result = await self.ap.persistence_mgr.execute_async(
                 sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
-                    persistence_pipeline.LegacyPipeline.uuid == update_data['use_pipeline_uuid']
+                    persistence_pipeline.LegacyPipeline.uuid == bot_data['use_pipeline_uuid']
                 )
             )
             pipeline = result.first()
             if pipeline is not None:
-                update_data['use_pipeline_name'] = pipeline.name
+                bot_data['use_pipeline_name'] = pipeline.name
+                # Also sync to binding_uuid if binding_type is 'pipeline' or not set
+                if binding_type is None or binding_type == 'pipeline':
+                    bot_data['binding_uuid'] = bot_data['use_pipeline_uuid']
+                    bot_data['binding_type'] = 'pipeline'
             else:
+                # Only raise error if binding_type is explicitly 'pipeline' or not set
+                if binding_type is None or binding_type == 'pipeline':
                     raise Exception('Pipeline not found')
+                # If binding_type is 'workflow', just clear the use_pipeline_uuid
+                bot_data['use_pipeline_uuid'] = None
+                bot_data['use_pipeline_name'] = None
+
+        # If binding_uuid is set directly (for workflow), clear pipeline fields
+        if 'binding_uuid' in bot_data and binding_type == 'workflow':
+            # For workflow binding, clear pipeline-related fields to avoid confusion
+            bot_data['binding_type'] = 'workflow'
+            bot_data['use_pipeline_uuid'] = None
+            bot_data['use_pipeline_name'] = None
 
         await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(persistence_bot.Bot).values(update_data).where(persistence_bot.Bot.uuid == bot_uuid)
+            sqlalchemy.update(persistence_bot.Bot).values(bot_data).where(persistence_bot.Bot.uuid == bot_uuid)
         )
         await self.ap.platform_mgr.remove_bot(bot_uuid)
 
@@ -199,3 +225,35 @@ class BotService:
 
         # Send message via adapter
         await runtime_bot.adapter.send_message(target_type, str(target_id), message_chain)
+
+    # ============ Bot Admins ============
+
+    async def get_bot_admins(self, bot_uuid: str) -> list[dict]:
+        from ....entity.persistence import bot as persistence_bot
+
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_bot.BotAdmin).where(persistence_bot.BotAdmin.bot_uuid == bot_uuid)
+        )
+        return [{'id': r.id, 'launcher_type': r.launcher_type, 'launcher_id': r.launcher_id} for r in result.all()]
+
+    async def add_bot_admin(self, bot_uuid: str, launcher_type: str, launcher_id: str) -> int:
+        from ....entity.persistence import bot as persistence_bot
+
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_bot.BotAdmin).values(
+                bot_uuid=bot_uuid,
+                launcher_type=launcher_type,
+                launcher_id=launcher_id,
+            )
+        )
+        return result.inserted_primary_key[0]
+
+    async def delete_bot_admin(self, bot_uuid: str, admin_id: int) -> None:
+        from ....entity.persistence import bot as persistence_bot
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_bot.BotAdmin).where(
+                persistence_bot.BotAdmin.bot_uuid == bot_uuid,
+                persistence_bot.BotAdmin.id == admin_id,
+            )
+        )

src/langbot/pkg/api/http/service/mcp.py

@@ -136,6 +136,32 @@ class MCPService:
             if server_name in self.ap.tool_mgr.mcp_tool_loader.sessions:
                 await self.ap.tool_mgr.mcp_tool_loader.remove_mcp_server(server_name)
 
+    async def get_mcp_server_resources(self, server_name: str) -> list[dict]:
+        """Get resources from a specific MCP server."""
+        return await self.ap.tool_mgr.mcp_tool_loader.get_resources(server_name)
+
+    async def get_mcp_server_resource_templates(self, server_name: str) -> list[dict]:
+        """Get resource templates from a specific MCP server."""
+        return await self.ap.tool_mgr.mcp_tool_loader.get_resource_templates(server_name)
+
+    async def read_mcp_server_resource_envelope(
+        self,
+        server_name: str,
+        uri: str,
+        *,
+        max_bytes: int | None = None,
+        include_blob: bool = False,
+    ) -> dict:
+        """Read a resource from a specific MCP server with metadata."""
+        kwargs = {'include_blob': include_blob, 'source': 'ui_preview'}
+        if max_bytes is not None:
+            kwargs['max_bytes'] = max_bytes
+        return await self.ap.tool_mgr.mcp_tool_loader.read_resource_envelope(server_name, uri, **kwargs)
+
+    async def read_mcp_server_resource(self, server_name: str, uri: str) -> list[dict]:
+        """Read a resource from a specific MCP server."""
+        return await self.ap.tool_mgr.mcp_tool_loader.read_resource(server_name, uri)
+
     async def test_mcp_server(self, server_name: str, server_data: dict) -> int:
         """测试 MCP 服务器连接并返回任务 ID"""
 

src/langbot/pkg/api/http/service/pipeline.py

@@ -73,6 +73,20 @@ class PipelineService:
 
         return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
 
+    async def get_pipeline_by_name(self, pipeline_name: str) -> dict | None:
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
+                persistence_pipeline.LegacyPipeline.name == pipeline_name
+            )
+        )
+
+        pipeline = result.first()
+
+        if pipeline is None:
+            return None
+
+        return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
+
     async def create_pipeline(self, pipeline_data: dict, default: bool = False) -> str:
         from ....utils import paths as path_utils
 
@@ -100,6 +114,8 @@ class PipelineService:
                 'enable_all_mcp_servers': True,
                 'plugins': [],
                 'mcp_servers': [],
+                'mcp_resources': [],
+                'mcp_resource_agent_read_enabled': True,
             }
 
         await self.ap.persistence_mgr.execute_async(
@@ -193,6 +209,8 @@ class PipelineService:
                     'enable_all_mcp_servers': True,
                     'plugins': [],
                     'mcp_servers': [],
+                    'mcp_resources': [],
+                    'mcp_resource_agent_read_enabled': True,
                 }
             ),
         }
@@ -217,6 +235,8 @@ class PipelineService:
         enable_all_mcp_servers: bool = True,
         bound_skills: list[str] = None,
         enable_all_skills: bool = True,
+        bound_mcp_resources: list[dict] = None,
+        mcp_resource_agent_read_enabled: bool | None = None,
     ) -> None:
         """Update the bound plugins and MCP servers for a pipeline"""
         # Get current pipeline
@@ -236,10 +256,14 @@ class PipelineService:
         extensions_preferences['enable_all_mcp_servers'] = enable_all_mcp_servers
         extensions_preferences['enable_all_skills'] = enable_all_skills
         extensions_preferences['plugins'] = bound_plugins
+        if mcp_resource_agent_read_enabled is not None:
+            extensions_preferences['mcp_resource_agent_read_enabled'] = mcp_resource_agent_read_enabled
         if bound_mcp_servers is not None:
             extensions_preferences['mcp_servers'] = bound_mcp_servers
         if bound_skills is not None:
             extensions_preferences['skills'] = bound_skills
+        if bound_mcp_resources is not None:
+            extensions_preferences['mcp_resources'] = bound_mcp_resources
 
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.update(persistence_pipeline.LegacyPipeline)

src/langbot/pkg/command/cmdmgr.py

@@ -84,7 +84,17 @@ class CommandManager:
 
         privilege = 1
 
-        if f'{query.launcher_type.value}_{query.launcher_id}' in self.ap.instance_config.data['admins']:
+        import sqlalchemy as _sa
+        from ..entity.persistence.bot import BotAdmin as _BotAdmin
+
+        _admins = await self.ap.persistence_mgr.execute_async(
+            _sa.select(_BotAdmin).where(
+                _BotAdmin.bot_uuid == (query.bot_uuid or ''),
+                _BotAdmin.launcher_type == query.launcher_type.value,
+                _BotAdmin.launcher_id == str(query.launcher_id),
+            )
+        )
+        if _admins.first() is not None:
             privilege = 2
 
         ctx = command_context.ExecuteContext(

src/langbot/pkg/core/app.py

@@ -32,6 +32,7 @@ from ..api.http.service import mcp as mcp_service
 from ..api.http.service import apikey as apikey_service
 from ..api.http.service import webhook as webhook_service
 from ..api.http.service import monitoring as monitoring_service
+from ..api.http.service import workflow as workflow_service
 from ..api.http.service import skill as skill_service
 from ..api.http.service import maintenance as maintenance_service
 from ..discover import engine as discover_engine
@@ -153,6 +154,8 @@ class Application:
 
     webhook_service: webhook_service.WebhookService = None
 
+    workflow_service: workflow_service.WorkflowService = None
+
     telemetry: telemetry_module.TelemetryManager = None
 
     survey: survey_module.SurveyManager = None
@@ -255,6 +258,22 @@ class Application:
                     scopes=[core_entities.LifecycleControlScope.APPLICATION],
                 )
 
+            async def workflow_execution_cleanup_loop():
+                check_interval_seconds = 60
+                while True:
+                    try:
+                        cancelled = await self.workflow_service.cleanup_stale_executions()
+                        if cancelled > 0:
+                            self.logger.info(f'Workflow execution auto-cleanup: cancelled {cancelled} stale executions')
+                    except Exception as e:
+                        self.logger.warning(f'Workflow execution auto-cleanup error: {e}')
+                    await asyncio.sleep(check_interval_seconds)
+
+            self.task_mgr.create_task(
+                workflow_execution_cleanup_loop(),
+                name='workflow-execution-cleanup',
+                scopes=[core_entities.LifecycleControlScope.APPLICATION],
+            )
             # Start storage/log maintenance task if enabled
             storage_cleanup_cfg = self.instance_config.data.get('storage', {}).get('cleanup', {})
             if storage_cleanup_cfg.get('enabled', True) and self.maintenance_service is not None:

src/langbot/pkg/core/stages/build_app.py

@@ -29,6 +29,7 @@ from ...api.http.service import mcp as mcp_service
 from ...api.http.service import apikey as apikey_service
 from ...api.http.service import webhook as webhook_service
 from ...api.http.service import monitoring as monitoring_service
+from ...api.http.service import workflow as workflow_service
 from ...api.http.service import skill as skill_service
 from ...skill import manager as skill_mgr
 from ...api.http.service import maintenance as maintenance_service
@@ -89,6 +90,9 @@ class BuildAppStage(stage.BootingStage):
         webhook_service_inst = webhook_service.WebhookService(ap)
         ap.webhook_service = webhook_service_inst
 
+        workflow_service_inst = workflow_service.WorkflowService(ap)
+        ap.workflow_service = workflow_service_inst
+
         skill_service_inst = skill_service.SkillService(ap)
         ap.skill_service = skill_service_inst
 

src/langbot/pkg/core/stages/load_config.py

@@ -231,3 +231,34 @@ class LoadConfigStage(stage.BootingStage):
         ap.pipeline_config_meta_safety = await load_resource_yaml_template_data('metadata/pipeline/safety.yaml')
         ap.pipeline_config_meta_ai = await load_resource_yaml_template_data('metadata/pipeline/ai.yaml')
         ap.pipeline_config_meta_output = await load_resource_yaml_template_data('metadata/pipeline/output.yaml')
+
+        # Load workflow node metadata from YAML files. YAML is the source of
+        # truth for workflow editor metadata; Python classes provide execution
+        # logic and are bound through the registry.
+        from langbot.pkg.workflow.metadata import NodeMetadataLoader
+        from langbot.pkg.workflow.registry import NodeTypeRegistry
+
+        workflow_metadata_loader = NodeMetadataLoader()
+        workflow_node_count = await workflow_metadata_loader.load_core_metadata()
+        ap.workflow_node_configs = workflow_metadata_loader.get_all_metadata()
+        ap.workflow_node_metadata_loader = workflow_metadata_loader
+
+        workflow_registry = NodeTypeRegistry.instance()
+        for node_config in ap.workflow_node_configs.values():
+            workflow_registry.register_metadata(node_config, source=node_config.get('_source', 'core'))
+
+        # Auto-discover and register workflow nodes using discovery engine
+        if hasattr(ap, 'discover') and ap.discover is not None:
+            workflow_registry.discover_nodes(ap.discover)
+
+        workflow_load_errors = workflow_metadata_loader.get_load_errors()
+        if workflow_load_errors:
+            print(f'Workflow node metadata load errors: {len(workflow_load_errors)}')
+            for error in workflow_load_errors:
+                print(f"  - {error.get('file')}: {error.get('error')}")
+
+        print(
+            f'Loaded {workflow_node_count} workflow node metadata files; '
+            f'registered {workflow_registry.metadata_count()} metadata definitions, '
+            f'{workflow_registry.count()} node types'
+        )

src/langbot/pkg/discover/engine.py

@@ -304,3 +304,65 @@ class ComponentDiscoveryEngine:
             if component.kind == kind:
                 result.append(component)
         return result
+
+    def discover_workflow_nodes(self, nodes_dir: str) -> typing.List[typing.Type]:
+        """Discover workflow node classes from a directory of Python modules.
+        
+        Scans all .py files in the given directory, imports them, and collects
+        classes that are subclasses of WorkflowNode.
+        
+        Args:
+            nodes_dir: Directory path like 'pkg/workflow/nodes/'
+            
+        Returns:
+            List of WorkflowNode subclasses found
+        """
+        from langbot.pkg.workflow.node import WorkflowNode
+        
+        node_classes: typing.List[typing.Type[WorkflowNode]] = []
+        
+        # Normalize path
+        if nodes_dir.endswith('/'):
+            nodes_dir = nodes_dir[:-1]
+        
+        # Import the nodes package to trigger all module imports
+        module_path = nodes_dir.replace('/', '.').replace('\\', '.')
+        package_path = module_path
+        
+        try:
+            # Import the package __init__ to trigger submodule imports
+            importlib.import_module(f'langbot.{package_path}')
+        except ImportError:
+            self.ap.logger.warning(f'Failed to import workflow nodes package: langbot.{package_path}')
+        
+        # Since workflow/__init__.py is empty, explicitly import all .py files in the nodes directory
+        import os
+        # engine.py is in langbot/pkg/discover/, nodes are in langbot/pkg/workflow/nodes/
+        nodes_abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'workflow', 'nodes'))
+        if os.path.isdir(nodes_abs_path):
+            for filename in os.listdir(nodes_abs_path):
+                if filename.endswith('.py') and not filename.startswith('_'):
+                    module_name = filename[:-3]
+                    try:
+                        importlib.import_module(f'langbot.{package_path}.{module_name}')
+                    except ImportError as e:
+                        self.ap.logger.warning(f'Failed to import workflow node module: {module_name}: {e}')
+        
+        # Now collect all WorkflowNode subclasses from sys.modules
+        import sys
+        prefix = f'langbot.{package_path}.'
+        for mod_name, mod in sys.modules.items():
+            if mod_name.startswith(prefix) and mod is not None:
+                for attr_name in dir(mod):
+                    attr = getattr(mod, attr_name)
+                    if (
+                        isinstance(attr, type)
+                        and issubclass(attr, WorkflowNode)
+                        and attr is not WorkflowNode
+                        and hasattr(attr, 'type_name')
+                        and attr.type_name
+                    ):
+                        if attr not in node_classes:
+                            node_classes.append(attr)
+        
+        return node_classes

src/langbot/pkg/entity/persistence/bot.py

@@ -3,6 +3,20 @@ import sqlalchemy
 from .base import Base
 
 
+class BotAdmin(Base):
+    """Bot admin — a launcher that has admin privilege for a specific bot's commands"""
+
+    __tablename__ = 'bot_admins'
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
+    bot_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    launcher_type = sqlalchemy.Column(sqlalchemy.String(64), nullable=False)
+    launcher_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+
+    __table_args__ = (sqlalchemy.UniqueConstraint('bot_uuid', 'launcher_type', 'launcher_id', name='uq_bot_admin'),)
+
+
 class Bot(Base):
     """Bot"""
 
@@ -17,6 +31,13 @@ class Bot(Base):
     use_pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     use_pipeline_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     pipeline_routing_rules = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, server_default='[]')
+
+    # New unified binding fields
+    # binding_type: 'pipeline' or 'workflow'
+    binding_type = sqlalchemy.Column(sqlalchemy.String(32), nullable=False, server_default='pipeline')
+    # binding_uuid: UUID of the bound Pipeline or Workflow
+    binding_uuid = sqlalchemy.Column(sqlalchemy.String(64), nullable=True)
+
     created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
     updated_at = sqlalchemy.Column(
         sqlalchemy.DateTime,

src/langbot/pkg/entity/persistence/pipeline.py

@@ -26,7 +26,14 @@ class LegacyPipeline(Base):
     extensions_preferences = sqlalchemy.Column(
         sqlalchemy.JSON,
         nullable=False,
-        default={'enable_all_plugins': True, 'enable_all_mcp_servers': True, 'plugins': [], 'mcp_servers': []},
+        default={
+            'enable_all_plugins': True,
+            'enable_all_mcp_servers': True,
+            'plugins': [],
+            'mcp_servers': [],
+            'mcp_resources': [],
+            'mcp_resource_agent_read_enabled': True,
+        },
     )
 
 

src/langbot/pkg/entity/persistence/workflow.py

@@ -0,0 +1,126 @@
+"""Workflow persistence entities"""
+
+import sqlalchemy
+
+from .base import Base
+
+
+class Workflow(Base):
+    """Workflow definition"""
+
+    __tablename__ = 'workflows'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    description = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔄')
+    version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=1)
+    is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
+
+    # Workflow definition stored as JSON
+    # Contains: nodes, edges, variables, settings
+    definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+
+    # Global config (inherited from Pipeline capabilities)
+    # Contains: safety, output configs
+    global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+
+    # Extensions preferences (same as Pipeline)
+    extensions_preferences = sqlalchemy.Column(
+        sqlalchemy.JSON,
+        nullable=False,
+        default={'enable_all_plugins': True, 'enable_all_mcp_servers': True, 'plugins': [], 'mcp_servers': []},
+    )
+
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+    updated_at = sqlalchemy.Column(
+        sqlalchemy.DateTime,
+        nullable=False,
+        server_default=sqlalchemy.func.now(),
+        onupdate=sqlalchemy.func.now(),
+    )
+
+
+class WorkflowVersion(Base):
+    """Workflow version history"""
+
+    __tablename__ = 'workflow_versions'
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
+    workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
+    global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+    created_by = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+
+    __table_args__ = (sqlalchemy.UniqueConstraint('workflow_uuid', 'version', name='uq_workflow_version'),)
+
+
+class WorkflowTrigger(Base):
+    """Workflow trigger configuration"""
+
+    __tablename__ = 'workflow_triggers'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # message, cron, event, webhook
+    config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
+    priority = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+    updated_at = sqlalchemy.Column(
+        sqlalchemy.DateTime,
+        nullable=False,
+        server_default=sqlalchemy.func.now(),
+        onupdate=sqlalchemy.func.now(),
+    )
+
+
+class WorkflowExecution(Base):
+    """Workflow execution record"""
+
+    __tablename__ = 'workflow_executions'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    workflow_version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False)  # pending, running, completed, failed, cancelled
+    trigger_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
+    trigger_data = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
+    variables = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
+    start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+
+
+class WorkflowNodeExecution(Base):
+    """Workflow node execution record"""
+
+    __tablename__ = 'workflow_node_executions'
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
+    execution_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    node_id = sqlalchemy.Column(sqlalchemy.String(100), nullable=False)
+    node_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
+    status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False)  # pending, running, completed, failed, skipped
+    inputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
+    outputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
+    start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    retry_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
+
+
+class ScheduledJob(Base):
+    """Scheduled job for cron triggers"""
+
+    __tablename__ = 'workflow_scheduled_jobs'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    trigger_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    cron_expression = sqlalchemy.Column(sqlalchemy.String(100), nullable=True)
+    next_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    last_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)

src/langbot/pkg/persistence/alembic/versions/0007_add_bot_admins.py

@@ -0,0 +1,84 @@
+"""add bot_admins table and migrate config admins
+
+Revision ID: 0007_add_bot_admins
+Revises: 0006_normalize_mcp_remote_mode
+Create Date: 2026-06-26
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = '0007_add_bot_admins'
+down_revision = '0006_normalize_mcp_remote_mode'
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    if 'bot_admins' in sa.inspect(conn).get_table_names():
+        return
+    op.create_table(
+        'bot_admins',
+        sa.Column('id', sa.Integer, primary_key=True, autoincrement=True),
+        sa.Column('bot_uuid', sa.String(255), nullable=False),
+        sa.Column('launcher_type', sa.String(64), nullable=False),
+        sa.Column('launcher_id', sa.String(255), nullable=False),
+        sa.Column('created_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
+        sa.UniqueConstraint('bot_uuid', 'launcher_type', 'launcher_id', name='uq_bot_admin'),
+    )
+
+    # Migrate old config-based admins into the first bot (best-effort)
+    inspector = sa.inspect(conn)
+    tables = inspector.get_table_names()
+
+    if 'bots' not in tables:
+        return
+
+    # Read the first bot uuid
+    row = conn.execute(sa.text('SELECT uuid FROM bots ORDER BY created_at LIMIT 1')).first()
+    if row is None:
+        return
+    first_bot_uuid = row[0]
+
+    # Read instance_config metadata key that holds the admins list
+    if 'metadata' not in tables:
+        return
+    meta_row = conn.execute(sa.text("SELECT value FROM metadata WHERE key = 'instance_config'")).first()
+    if meta_row is None:
+        return
+
+    import json
+
+    try:
+        cfg = json.loads(meta_row[0])
+    except Exception:
+        return
+
+    admins = cfg.get('admins', [])
+    for entry in admins:
+        parts = entry.split('_', 1)
+        if len(parts) != 2:
+            continue
+        launcher_type, launcher_id = parts
+        try:
+            conn.execute(
+                sa.text(
+                    'INSERT OR IGNORE INTO bot_admins (bot_uuid, launcher_type, launcher_id) VALUES (:bu, :lt, :li)'
+                ),
+                {'bu': first_bot_uuid, 'lt': launcher_type, 'li': launcher_id},
+            )
+        except Exception:
+            pass
+
+    # Remove admins key from stored config
+    if 'admins' in cfg:
+        del cfg['admins']
+        conn.execute(
+            sa.text("UPDATE metadata SET value = :v WHERE key = 'instance_config'"),
+            {'v': json.dumps(cfg)},
+        )
+
+
+def downgrade() -> None:
+    op.drop_table('bot_admins')

src/langbot/pkg/persistence/alembic/versions/0008_add_mcp_resource_preferences.py

@@ -0,0 +1,95 @@
+"""add mcp resource preferences to pipelines
+
+Revision ID: 0008_mcp_resource_prefs
+Revises: 0007_add_bot_admins
+Create Date: 2026-06-30
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = '0008_mcp_resource_prefs'
+down_revision = '0007_add_bot_admins'
+branch_labels = None
+depends_on = None
+
+
+_PIPELINE_TABLE = sa.table(
+    'legacy_pipelines',
+    sa.column('uuid', sa.String(255)),
+    sa.column('extensions_preferences', sa.JSON()),
+)
+
+
+def _has_extensions_preferences_table(conn: sa.Connection) -> bool:
+    inspector = sa.inspect(conn)
+    if 'legacy_pipelines' not in inspector.get_table_names():
+        return False
+    columns = {column['name'] for column in inspector.get_columns('legacy_pipelines')}
+    return 'extensions_preferences' in columns
+
+
+def _decode_preferences(value: Any) -> dict[str, Any]:
+    if value is None:
+        return {}
+    if isinstance(value, dict):
+        return dict(value)
+    if isinstance(value, str):
+        try:
+            decoded = json.loads(value)
+        except json.JSONDecodeError:
+            return {}
+        if isinstance(decoded, dict):
+            return decoded
+    return {}
+
+
+def _update_preferences(conn: sa.Connection, uuid: str, preferences: dict[str, Any]) -> None:
+    conn.execute(
+        _PIPELINE_TABLE.update().where(_PIPELINE_TABLE.c.uuid == uuid).values(extensions_preferences=preferences)
+    )
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    if not _has_extensions_preferences_table(conn):
+        return
+
+    rows = conn.execute(sa.select(_PIPELINE_TABLE.c.uuid, _PIPELINE_TABLE.c.extensions_preferences)).all()
+    for uuid, raw_preferences in rows:
+        preferences = _decode_preferences(raw_preferences)
+        changed = False
+
+        if 'mcp_resources' not in preferences:
+            preferences['mcp_resources'] = []
+            changed = True
+        if 'mcp_resource_agent_read_enabled' not in preferences:
+            preferences['mcp_resource_agent_read_enabled'] = True
+            changed = True
+
+        if changed:
+            _update_preferences(conn, uuid, preferences)
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    if not _has_extensions_preferences_table(conn):
+        return
+
+    rows = conn.execute(sa.select(_PIPELINE_TABLE.c.uuid, _PIPELINE_TABLE.c.extensions_preferences)).all()
+    for uuid, raw_preferences in rows:
+        preferences = _decode_preferences(raw_preferences)
+        changed = False
+
+        for key in ('mcp_resources', 'mcp_resource_agent_read_enabled'):
+            if key in preferences:
+                preferences.pop(key)
+                changed = True
+
+        if changed:
+            _update_preferences(conn, uuid, preferences)

src/langbot/pkg/persistence/alembic/versions/0009_add_workflow_tables.py

@@ -0,0 +1,207 @@
+"""add workflow tables and bot binding fields
+
+Revision ID: 0009_add_workflow_tables
+Revises: 0008_mcp_resource_prefs
+Create Date: 2026-07-01
+"""
+
+from __future__ import annotations
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = '0009_add_workflow_tables'
+down_revision = '0008_mcp_resource_prefs'
+branch_labels = None
+depends_on = None
+
+
+def _table_exists(conn: sa.Connection, table_name: str) -> bool:
+    return table_name in sa.inspect(conn).get_table_names()
+
+
+def _has_column(conn: sa.Connection, table_name: str, column_name: str) -> bool:
+    if not _table_exists(conn, table_name):
+        return False
+    return column_name in {column['name'] for column in sa.inspect(conn).get_columns(table_name)}
+
+
+def _has_index_for_columns(conn: sa.Connection, table_name: str, columns: tuple[str, ...]) -> bool:
+    if not _table_exists(conn, table_name):
+        return False
+    for index in sa.inspect(conn).get_indexes(table_name):
+        if tuple(index.get('column_names') or ()) == columns:
+            return True
+    return False
+
+
+def _ensure_index(conn: sa.Connection, table_name: str, index_name: str, columns: list[str]) -> None:
+    if _has_index_for_columns(conn, table_name, tuple(columns)):
+        return
+    op.create_index(index_name, table_name, columns)
+
+
+def _create_workflow_tables(conn: sa.Connection) -> None:
+    if not _table_exists(conn, 'workflows'):
+        op.create_table(
+            'workflows',
+            sa.Column('uuid', sa.String(255), primary_key=True),
+            sa.Column('name', sa.String(255), nullable=False),
+            sa.Column('description', sa.Text(), nullable=True),
+            sa.Column('emoji', sa.String(10), nullable=True),
+            sa.Column('version', sa.Integer(), nullable=False, server_default='1'),
+            sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
+            sa.Column('definition', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
+            sa.Column('global_config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
+            sa.Column(
+                'extensions_preferences',
+                sa.JSON(),
+                nullable=False,
+                server_default=sa.text(
+                    '\'{"enable_all_plugins": true, "enable_all_mcp_servers": true, "plugins": [], "mcp_servers": []}\''
+                ),
+            ),
+            sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+            sa.Column('updated_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+        )
+
+    if not _table_exists(conn, 'workflow_versions'):
+        op.create_table(
+            'workflow_versions',
+            sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
+            sa.Column('workflow_uuid', sa.String(255), nullable=False),
+            sa.Column('version', sa.Integer(), nullable=False),
+            sa.Column('definition', sa.JSON(), nullable=False),
+            sa.Column('global_config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
+            sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+            sa.Column('created_by', sa.String(255), nullable=True),
+            sa.UniqueConstraint('workflow_uuid', 'version', name='uq_workflow_version'),
+        )
+
+    if not _table_exists(conn, 'workflow_triggers'):
+        op.create_table(
+            'workflow_triggers',
+            sa.Column('uuid', sa.String(255), primary_key=True),
+            sa.Column('workflow_uuid', sa.String(255), nullable=False),
+            sa.Column('type', sa.String(50), nullable=False),
+            sa.Column('config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
+            sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
+            sa.Column('priority', sa.Integer(), nullable=False, server_default='0'),
+            sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+            sa.Column('updated_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+        )
+
+    if not _table_exists(conn, 'workflow_executions'):
+        op.create_table(
+            'workflow_executions',
+            sa.Column('uuid', sa.String(255), primary_key=True),
+            sa.Column('workflow_uuid', sa.String(255), nullable=False),
+            sa.Column('workflow_version', sa.Integer(), nullable=False),
+            sa.Column('status', sa.String(20), nullable=False),
+            sa.Column('trigger_type', sa.String(50), nullable=True),
+            sa.Column('trigger_data', sa.JSON(), nullable=True),
+            sa.Column('variables', sa.JSON(), nullable=True),
+            sa.Column('start_time', sa.DateTime(), nullable=True),
+            sa.Column('end_time', sa.DateTime(), nullable=True),
+            sa.Column('error', sa.Text(), nullable=True),
+            sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
+        )
+
+    if not _table_exists(conn, 'workflow_node_executions'):
+        op.create_table(
+            'workflow_node_executions',
+            sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
+            sa.Column('execution_uuid', sa.String(255), nullable=False),
+            sa.Column('node_id', sa.String(100), nullable=False),
+            sa.Column('node_type', sa.String(50), nullable=False),
+            sa.Column('status', sa.String(20), nullable=False),
+            sa.Column('inputs', sa.JSON(), nullable=True),
+            sa.Column('outputs', sa.JSON(), nullable=True),
+            sa.Column('start_time', sa.DateTime(), nullable=True),
+            sa.Column('end_time', sa.DateTime(), nullable=True),
+            sa.Column('error', sa.Text(), nullable=True),
+            sa.Column('retry_count', sa.Integer(), nullable=False, server_default='0'),
+        )
+
+    if not _table_exists(conn, 'workflow_scheduled_jobs'):
+        op.create_table(
+            'workflow_scheduled_jobs',
+            sa.Column('uuid', sa.String(255), primary_key=True),
+            sa.Column('trigger_uuid', sa.String(255), nullable=False),
+            sa.Column('cron_expression', sa.String(100), nullable=True),
+            sa.Column('next_run_time', sa.DateTime(), nullable=True),
+            sa.Column('last_run_time', sa.DateTime(), nullable=True),
+            sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
+        )
+
+    _ensure_index(conn, 'workflow_versions', 'ix_workflow_versions_workflow_uuid', ['workflow_uuid'])
+    _ensure_index(conn, 'workflow_triggers', 'ix_workflow_triggers_workflow_uuid', ['workflow_uuid'])
+    _ensure_index(conn, 'workflow_executions', 'ix_workflow_executions_workflow_uuid', ['workflow_uuid'])
+    _ensure_index(
+        conn,
+        'workflow_node_executions',
+        'ix_workflow_node_executions_execution_uuid',
+        ['execution_uuid'],
+    )
+    _ensure_index(conn, 'workflow_scheduled_jobs', 'ix_workflow_scheduled_jobs_trigger_uuid', ['trigger_uuid'])
+
+
+def _add_bot_binding_fields(conn: sa.Connection) -> None:
+    if not _table_exists(conn, 'bots'):
+        return
+
+    if not _has_column(conn, 'bots', 'binding_type'):
+        op.add_column(
+            'bots',
+            sa.Column('binding_type', sa.String(32), nullable=False, server_default='pipeline'),
+        )
+
+    if not _has_column(conn, 'bots', 'binding_uuid'):
+        op.add_column('bots', sa.Column('binding_uuid', sa.String(64), nullable=True))
+
+    conn.execute(
+        sa.text("""
+        UPDATE bots
+        SET binding_uuid = use_pipeline_uuid
+        WHERE use_pipeline_uuid IS NOT NULL
+          AND use_pipeline_uuid != ''
+          AND (binding_uuid IS NULL OR binding_uuid = '')
+        """)
+    )
+    conn.execute(
+        sa.text("""
+        UPDATE bots
+        SET binding_type = 'pipeline'
+        WHERE binding_uuid IS NOT NULL
+          AND binding_uuid != ''
+          AND (binding_type IS NULL OR binding_type = '')
+        """)
+    )
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    _create_workflow_tables(conn)
+    _add_bot_binding_fields(conn)
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+
+    if _has_column(conn, 'bots', 'binding_uuid'):
+        with op.batch_alter_table('bots') as batch_op:
+            batch_op.drop_column('binding_uuid')
+    if _has_column(conn, 'bots', 'binding_type'):
+        with op.batch_alter_table('bots') as batch_op:
+            batch_op.drop_column('binding_type')
+
+    for table_name in (
+        'workflow_scheduled_jobs',
+        'workflow_node_executions',
+        'workflow_executions',
+        'workflow_triggers',
+        'workflow_versions',
+        'workflows',
+    ):
+        if _table_exists(conn, table_name):
+            op.drop_table(table_name)

src/langbot/pkg/pipeline/monitor.py

@@ -32,7 +32,7 @@ class MonitoringHelper:
         """Record the start of query processing, returns message_id"""
         try:
             # Check if session exists, if not, record session start
-            session_id = f'{query.launcher_type}_{query.launcher_id}'
+            session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
 
             # Get sender name from message event
             sender_name = None
@@ -137,7 +137,7 @@ class MonitoringHelper:
     ):
         """Record bot response message to monitoring"""
         try:
-            session_id = f'{query.launcher_type}_{query.launcher_id}'
+            session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
 
             # Get sender name from message event
             sender_name = None
@@ -202,7 +202,7 @@ class MonitoringHelper:
     ) -> str:
         """Record query processing error, returns message_id"""
         try:
-            session_id = f'{query.launcher_type}_{query.launcher_id}'
+            session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
 
             # Get sender name from message event
             sender_name = None
@@ -268,7 +268,7 @@ class MonitoringHelper:
     ):
         """Record LLM call"""
         try:
-            session_id = f'{query.launcher_type}_{query.launcher_id}'
+            session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
 
             await ap.monitoring_service.record_llm_call(
                 bot_id=bot_id,

src/langbot/pkg/pipeline/pipelinemgr.py

@@ -13,7 +13,7 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.events as events
 from ..utils import importutil
-from .config_coercion import coerce_pipeline_config
+from .config import coerce_pipeline_config
 
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -96,6 +96,15 @@ class RuntimePipeline:
         extensions_prefs = pipeline_entity.extensions_preferences or {}
         self.enable_all_plugins = extensions_prefs.get('enable_all_plugins', True)
         self.enable_all_mcp_servers = extensions_prefs.get('enable_all_mcp_servers', True)
+        local_agent_config = (pipeline_entity.config or {}).get('ai', {}).get('local-agent', {})
+        self.mcp_resource_attachments = local_agent_config.get(
+            'mcp-resources',
+            extensions_prefs.get('mcp_resources', []),
+        )
+        self.mcp_resource_agent_read_enabled = local_agent_config.get(
+            'mcp-resource-agent-read-enabled',
+            extensions_prefs.get('mcp_resource_agent_read_enabled', True),
+        )
 
         if self.enable_all_plugins:
             # None indicates to use all available plugins
@@ -116,6 +125,8 @@ class RuntimePipeline:
         # Store bound plugins and MCP servers in query for filtering
         query.variables['_pipeline_bound_plugins'] = self.bound_plugins
         query.variables['_pipeline_bound_mcp_servers'] = self.bound_mcp_servers
+        query.variables['_pipeline_mcp_resource_attachments'] = self.mcp_resource_attachments
+        query.variables['_pipeline_mcp_resource_agent_read_enabled'] = self.mcp_resource_agent_read_enabled
 
         # Record query start for monitoring
         try:
@@ -178,7 +189,7 @@ class RuntimePipeline:
                 bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
                 pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
                 message_id = query.variables.get('_monitoring_message_id', '')
-                session_id = f'{query.launcher_type}_{query.launcher_id}'
+                session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
 
                 # Update message status to error
                 if message_id:
@@ -284,9 +295,9 @@ class RuntimePipeline:
         # Record query start and store message_id
         message_id = ''
         try:
-            from . import monitoring_helper
+            from . import monitor
 
-            message_id = await monitoring_helper.MonitoringHelper.record_query_start(
+            message_id = await monitor.MonitoringHelper.record_query_start(
                 ap=self.ap,
                 query=query,
                 bot_id=query.bot_uuid or 'unknown',
@@ -338,7 +349,7 @@ class RuntimePipeline:
             # Record query success only if no error occurred during processing
             if not query.variables.get('_monitoring_has_error', False):
                 try:
-                    await monitoring_helper.MonitoringHelper.record_query_success(
+                    await monitor.MonitoringHelper.record_query_success(
                         ap=self.ap,
                         message_id=message_id,
                         query=query,
@@ -348,7 +359,7 @@ class RuntimePipeline:
 
                 # Record bot response message
                 try:
-                    await monitoring_helper.MonitoringHelper.record_query_response(
+                    await monitor.MonitoringHelper.record_query_response(
                         ap=self.ap,
                         query=query,
                         bot_id=query.bot_uuid or 'unknown',
@@ -367,9 +378,9 @@ class RuntimePipeline:
 
             # Record query error
             try:
-                from . import monitoring_helper
+                from . import monitor
 
-                await monitoring_helper.MonitoringHelper.record_query_error(
+                await monitor.MonitoringHelper.record_query_error(
                     ap=self.ap,
                     query=query,
                     bot_id=query.bot_uuid or 'unknown',
@@ -384,7 +395,8 @@ class RuntimePipeline:
 
         finally:
             self.ap.logger.debug(f'Query {query.query_id} processed')
-            del self.ap.query_pool.cached_queries[query.query_id]
+            # Use pop with default to avoid KeyError if query was never cached
+            self.ap.query_pool.cached_queries.pop(query.query_id, None)
 
 
 class PipelineManager:

src/langbot/pkg/pipeline/plugin_diagnostics.py

@@ -0,0 +1,274 @@
+from __future__ import annotations
+
+import traceback
+import weakref
+from dataclasses import dataclass, field
+from typing import Any
+
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.platform.message as platform_message
+
+
+@dataclass(frozen=True)
+class PluginResponseSource:
+    plugin: dict[str, str]
+    event_name: str | None = None
+    is_approximate: bool = False
+
+
+@dataclass
+class QueryDiagnosticState:
+    pending_by_chain_id: dict[int, list[PluginResponseSource]] = field(default_factory=dict)
+    by_response_index: dict[int, list[PluginResponseSource]] = field(default_factory=dict)
+    finalizer: weakref.finalize | None = None
+
+
+_QUERY_STATES: dict[int, QueryDiagnosticState] = {}
+
+
+def record_plugin_response_source(
+    query: pipeline_query.Query,
+    response_index: int,
+    response_sources: list[dict[str, Any]] | None,
+    emitted_plugins: list[dict[str, Any]] | None = None,
+    event_name: str | None = None,
+) -> None:
+    plugin_sources = _build_plugin_sources(response_sources, emitted_plugins, event_name)
+    if not plugin_sources:
+        return
+    state = _get_or_create_query_state(query)
+    state.by_response_index[response_index] = plugin_sources
+
+
+def record_last_plugin_response_source(
+    query: pipeline_query.Query,
+    response_sources: list[dict[str, Any]] | None,
+    emitted_plugins: list[dict[str, Any]] | None = None,
+    event_name: str | None = None,
+) -> None:
+    record_plugin_response_source(
+        query,
+        len(query.resp_message_chain) - 1,
+        response_sources,
+        emitted_plugins,
+        event_name,
+    )
+
+
+def record_pending_plugin_response_source(
+    query: pipeline_query.Query,
+    message_chain: platform_message.MessageChain,
+    response_sources: list[dict[str, Any]] | None,
+    emitted_plugins: list[dict[str, Any]] | None = None,
+    event_name: str | None = None,
+) -> None:
+    plugin_sources = _build_plugin_sources(response_sources, emitted_plugins, event_name)
+    if not plugin_sources:
+        return
+    state = _get_or_create_query_state(query)
+    state.pending_by_chain_id[id(message_chain)] = plugin_sources
+
+
+def consume_pending_plugin_response_source(
+    query: pipeline_query.Query,
+    message_chain: platform_message.MessageChain,
+    response_index: int,
+) -> None:
+    state = _get_query_state(query)
+    if state is None:
+        return
+    source = state.pending_by_chain_id.pop(id(message_chain), None)
+    if source is None:
+        return
+    state.by_response_index[response_index] = source
+
+
+def clear_response_source(query: pipeline_query.Query, response_index: int) -> None:
+    state = _get_query_state(query)
+    if state is None:
+        return
+    state.by_response_index.pop(response_index, None)
+    _discard_query_state_if_empty(query)
+
+
+async def notify_response_delivery_failure(
+    ap: Any,
+    query: pipeline_query.Query,
+    response_index: int,
+    message_chain: platform_message.MessageChain,
+    error: Exception,
+) -> None:
+    try:
+        plugin_refs = _get_response_sources(query, response_index)
+        if not plugin_refs:
+            return
+        connector = getattr(ap, 'plugin_connector', None)
+        if connector is None or not hasattr(connector, 'notify_plugin_diagnostic'):
+            return
+        for source in plugin_refs:
+            payload = _build_delivery_failure_payload(
+                plugin_ref=source.plugin,
+                event_name=source.event_name,
+                is_approximate=source.is_approximate,
+                query=query,
+                response_index=response_index,
+                message_chain=message_chain,
+                error=error,
+            )
+            try:
+                await connector.notify_plugin_diagnostic(payload)
+            except Exception as diag_error:
+                _debug(ap, f'Plugin diagnostic forwarding failed: {diag_error}')
+    except Exception as diag_error:
+        _debug(ap, f'Plugin diagnostic forwarding skipped: {diag_error}')
+
+
+def get_emitted_plugins(event_ctx: Any) -> list[dict[str, Any]]:
+    emitted_plugins = getattr(event_ctx, '_emitted_plugins', [])
+    return emitted_plugins if isinstance(emitted_plugins, list) else []
+
+
+def get_response_sources(event_ctx: Any) -> list[dict[str, Any]] | None:
+    event_attrs = vars(event_ctx)
+    if '_response_sources' not in event_attrs:
+        return None
+    response_sources = event_attrs['_response_sources']
+    return response_sources if isinstance(response_sources, list) else []
+
+
+def _get_or_create_query_state(query: pipeline_query.Query) -> QueryDiagnosticState:
+    query_key = id(query)
+    state = _QUERY_STATES.get(query_key)
+    if state is not None:
+        return state
+
+    state = QueryDiagnosticState()
+    try:
+        state.finalizer = weakref.finalize(query, _discard_query_state, query_key)
+    except TypeError:
+        state.finalizer = None
+    _QUERY_STATES[query_key] = state
+    return state
+
+
+def _get_query_state(query: pipeline_query.Query) -> QueryDiagnosticState | None:
+    return _QUERY_STATES.get(id(query))
+
+
+def _discard_query_state(query_key: int) -> None:
+    _QUERY_STATES.pop(query_key, None)
+
+
+def _discard_query_state_if_empty(query: pipeline_query.Query) -> None:
+    query_key = id(query)
+    state = _QUERY_STATES.get(query_key)
+    if state is None:
+        return
+    if state.pending_by_chain_id or state.by_response_index:
+        return
+    if state.finalizer is not None:
+        state.finalizer.detach()
+    _discard_query_state(query_key)
+
+
+def _get_response_sources(
+    query: pipeline_query.Query,
+    response_index: int,
+) -> list[PluginResponseSource]:
+    state = _get_query_state(query)
+    if state is None:
+        return []
+    return state.by_response_index.get(response_index, [])
+
+
+def _extract_plugin_ref(plugin: Any) -> dict[str, str] | None:
+    manifest = plugin.get('manifest') if isinstance(plugin, dict) else None
+    metadata = manifest.get('metadata') if isinstance(manifest, dict) else None
+    if not isinstance(metadata, dict):
+        return None
+    author = metadata.get('author')
+    name = metadata.get('name')
+    if not author or not name:
+        return None
+    return {'author': str(author), 'name': str(name)}
+
+
+def _extract_response_source_plugin_ref(source: Any) -> dict[str, str] | None:
+    if not isinstance(source, dict):
+        return None
+    if source.get('kind') != 'reply_message_chain':
+        return None
+    plugin_ref = source.get('plugin')
+    if not isinstance(plugin_ref, dict):
+        return None
+    author = plugin_ref.get('author')
+    name = plugin_ref.get('name')
+    if not author or not name:
+        return None
+    return {'author': str(author), 'name': str(name)}
+
+
+def _build_plugin_sources(
+    response_sources: list[dict[str, Any]] | None,
+    emitted_plugins: list[dict[str, Any]] | None,
+    event_name: str | None,
+) -> list[PluginResponseSource]:
+    if response_sources is not None:
+        plugin_refs = [_extract_response_source_plugin_ref(source) for source in response_sources]
+        return [
+            PluginResponseSource(plugin=plugin, event_name=event_name) for plugin in plugin_refs if plugin is not None
+        ]
+
+    if emitted_plugins:
+        plugin_refs = [_extract_plugin_ref(plugin) for plugin in emitted_plugins]
+        return [
+            PluginResponseSource(plugin=plugin, event_name=event_name, is_approximate=True)
+            for plugin in plugin_refs
+            if plugin is not None
+        ]
+    return []
+
+
+def _debug(ap: Any, message: str) -> None:
+    logger = getattr(ap, 'logger', None)
+    if logger is not None:
+        logger.debug(message)
+
+
+def _build_delivery_failure_payload(
+    plugin_ref: dict[str, str],
+    event_name: str | None,
+    is_approximate: bool,
+    query: pipeline_query.Query,
+    response_index: int,
+    message_chain: platform_message.MessageChain,
+    error: Exception,
+) -> dict[str, Any]:
+    details: dict[str, Any] = {
+        'message_component_types': [component.__class__.__name__ for component in message_chain],
+        'message_preview': str(message_chain)[:200],
+    }
+    if is_approximate:
+        details['attribution_warning'] = (
+            'This diagnostic was delivered to all plugins that handled the event because the '
+            'plugin runtime did not report the exact reply_message_chain source.'
+        )
+
+    return {
+        'level': 'ERROR',
+        'code': 'response_delivery_failed',
+        'message': 'Failed to deliver a plugin-provided response message.',
+        'plugin': plugin_ref,
+        'query': {
+            'query_id': query.query_id,
+            'event_name': event_name or query.message_event.__class__.__name__,
+            'stage': query.current_stage_name or 'SendResponseBackStage',
+            'response_index': response_index,
+        },
+        'details': details,
+        'delivery': {
+            'error_type': error.__class__.__name__,
+            'error_message': str(error),
+            'traceback': traceback.format_exception_only(type(error), error)[-1].strip(),
+        },
+    }

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -25,6 +25,21 @@ class PreProcessor(stage.PipelineStage):
         - use_funcs
     """
 
+    @staticmethod
+    def _filter_selected_tools(
+        tools: list,
+        local_agent_config: dict,
+    ) -> list:
+        if local_agent_config.get('enable-all-tools', True) is not False:
+            return tools
+
+        selected_tools = local_agent_config.get('tools', [])
+        if not isinstance(selected_tools, list):
+            return []
+
+        selected_tool_names = {tool for tool in selected_tools if isinstance(tool, str)}
+        return [tool for tool in tools if tool.name in selected_tool_names]
+
     async def process(
         self,
         query: pipeline_query.Query,
@@ -32,6 +47,7 @@ class PreProcessor(stage.PipelineStage):
     ) -> entities.StageProcessResult:
         """Process"""
         selected_runner = query.pipeline_config['ai']['runner']['runner']
+        local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
         include_skill_authoring = (
             selected_runner == 'local-agent' and getattr(self.ap, 'skill_service', None) is not None
         )
@@ -43,7 +59,7 @@ class PreProcessor(stage.PipelineStage):
         if selected_runner == 'local-agent':
             # Read model config — new format is { primary: str, fallbacks: [str] },
             # but handle legacy plain string for backward compatibility
-            model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
+            model_config = local_agent_config.get('model', {})
             if isinstance(model_config, str):
                 # Legacy format: plain UUID string
                 primary_uuid = model_config
@@ -113,11 +129,14 @@ class PreProcessor(stage.PipelineStage):
                     # Get bound plugins and MCP servers for filtering tools
                     bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
                     bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
-                    query.use_funcs = await self.ap.tool_mgr.get_all_tools(
+                    include_mcp_resource_tools = query.variables.get('_pipeline_mcp_resource_agent_read_enabled', True)
+                    all_tools = await self.ap.tool_mgr.get_all_tools(
                         bound_plugins,
                         bound_mcp_servers,
                         include_skill_authoring=include_skill_authoring,
+                        include_mcp_resource_tools=include_mcp_resource_tools,
                     )
+                    query.use_funcs = self._filter_selected_tools(all_tools, local_agent_config)
 
                     self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
                     self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
@@ -128,11 +147,14 @@ class PreProcessor(stage.PipelineStage):
             if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
                 bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
                 bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
-                query.use_funcs = await self.ap.tool_mgr.get_all_tools(
+                include_mcp_resource_tools = query.variables.get('_pipeline_mcp_resource_agent_read_enabled', True)
+                all_tools = await self.ap.tool_mgr.get_all_tools(
                     bound_plugins,
                     bound_mcp_servers,
                     include_skill_authoring=include_skill_authoring,
+                    include_mcp_resource_tools=include_mcp_resource_tools,
                 )
+                query.use_funcs = self._filter_selected_tools(all_tools, local_agent_config)
 
         sender_name = ''
 

src/langbot/pkg/pipeline/process/handlers/chat.py

@@ -9,6 +9,7 @@ from datetime import datetime
 
 from .. import handler
 from ... import entities
+from ... import plugin_diagnostics
 from ....provider import runner as runner_module
 
 import langbot_plugin.api.entities.events as events
@@ -58,6 +59,13 @@ class ChatMessageHandler(handler.MessageHandler):
         if event_ctx.is_prevented_default():
             if event_ctx.event.reply_message_chain is not None:
                 mc = event_ctx.event.reply_message_chain
+                plugin_diagnostics.record_pending_plugin_response_source(
+                    query,
+                    mc,
+                    plugin_diagnostics.get_response_sources(event_ctx),
+                    plugin_diagnostics.get_emitted_plugins(event_ctx),
+                    event.event_name,
+                )
                 query.resp_messages.append(mc)
 
                 yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/pipeline/process/handlers/command.py

@@ -4,6 +4,7 @@ import typing
 
 from .. import handler
 from ... import entities
+from ... import plugin_diagnostics
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -52,6 +53,13 @@ class CommandHandler(handler.MessageHandler):
         if event_ctx.is_prevented_default():
             if event_ctx.event.reply_message_chain is not None:
                 mc = event_ctx.event.reply_message_chain
+                plugin_diagnostics.record_pending_plugin_response_source(
+                    query,
+                    mc,
+                    plugin_diagnostics.get_response_sources(event_ctx),
+                    plugin_diagnostics.get_emitted_plugins(event_ctx),
+                    event.event_name,
+                )
 
                 query.resp_messages.append(mc)
 

src/langbot/pkg/pipeline/respback/respback.py

@@ -9,6 +9,7 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 from .. import stage, entities
+from .. import plugin_diagnostics
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 
 
@@ -39,20 +40,35 @@ class SendResponseBackStage(stage.PipelineStage):
 
         has_chunks = any(isinstance(msg, provider_message.MessageChunk) for msg in query.resp_messages)
         # TODO 命令与流式的兼容性问题
+        response_index = len(query.resp_message_chain) - 1
+        message_chain = query.resp_message_chain[-1]
+
+        try:
             if await query.adapter.is_stream_output_supported() and has_chunks:
                 is_final = [msg.is_final for msg in query.resp_messages][0]
                 await query.adapter.reply_message_chunk(
                     message_source=query.message_event,
                     bot_message=query.resp_messages[-1],
-                message=query.resp_message_chain[-1],
+                    message=message_chain,
                     quote_origin=quote_origin,
                     is_final=is_final,
                 )
             else:
                 await query.adapter.reply_message(
                     message_source=query.message_event,
-                message=query.resp_message_chain[-1],
+                    message=message_chain,
                     quote_origin=quote_origin,
                 )
+        except Exception as e:
+            await plugin_diagnostics.notify_response_delivery_failure(
+                self.ap,
+                query,
+                response_index,
+                message_chain,
+                e,
+            )
+            plugin_diagnostics.clear_response_source(query, response_index)
+            raise
+        plugin_diagnostics.clear_response_source(query, response_index)
 
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/pipeline/wrapper/wrapper.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import typing
 
 from .. import entities
+from .. import plugin_diagnostics
 from .. import stage
 
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
@@ -78,6 +79,11 @@ class ResponseWrapper(stage.PipelineStage):
         # 如果 resp_messages[-1] 已经是 MessageChain 了
         if isinstance(query.resp_messages[-1], platform_message.MessageChain):
             query.resp_message_chain.append(query.resp_messages[-1])
+            plugin_diagnostics.consume_pending_plugin_response_source(
+                query,
+                query.resp_messages[-1],
+                len(query.resp_message_chain) - 1,
+            )
 
             yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
 
@@ -129,8 +135,10 @@ class ResponseWrapper(stage.PipelineStage):
                         else:
                             if event_ctx.event.reply_message_chain is not None:
                                 reply_chain = event_ctx.event.reply_message_chain
+                                is_plugin_reply = True
                             else:
                                 reply_chain = result.get_content_platform_message_chain()
+                                is_plugin_reply = False
 
                             # Attach files the agent produced in the sandbox
                             # outbox, but only on the terminal assistant message.
@@ -138,6 +146,13 @@ class ResponseWrapper(stage.PipelineStage):
                                 await self._append_outbound_attachments(query, reply_chain)
 
                             query.resp_message_chain.append(reply_chain)
+                            if is_plugin_reply:
+                                plugin_diagnostics.record_last_plugin_response_source(
+                                    query,
+                                    plugin_diagnostics.get_response_sources(event_ctx),
+                                    plugin_diagnostics.get_emitted_plugins(event_ctx),
+                                    event.event_name,
+                                )
 
                             yield entities.StageProcessResult(
                                 result_type=entities.ResultType.CONTINUE,
@@ -180,6 +195,12 @@ class ResponseWrapper(stage.PipelineStage):
                             else:
                                 if event_ctx.event.reply_message_chain is not None:
                                     query.resp_message_chain.append(event_ctx.event.reply_message_chain)
+                                    plugin_diagnostics.record_last_plugin_response_source(
+                                        query,
+                                        plugin_diagnostics.get_response_sources(event_ctx),
+                                        plugin_diagnostics.get_emitted_plugins(event_ctx),
+                                        event.event_name,
+                                    )
 
                                 else:
                                     query.resp_message_chain.append(

src/langbot/pkg/platform/botmgr.py

@@ -2,12 +2,14 @@ from __future__ import annotations
 
 import asyncio
 import json
-import re
+import logging
 import traceback
 import sqlalchemy
 
 from ..core import app, entities as core_entities, taskmgr
 
+logger = logging.getLogger(__name__)
+
 from ..discover import engine
 
 from ..entity.persistence import bot as persistence_bot
@@ -54,29 +56,24 @@ class RuntimeBot:
         self.task_context = taskmgr.TaskContext()
         self.logger = logger
 
-    @staticmethod
-    def _match_operator(actual: str, operator: str, expected: str) -> bool:
-        """Evaluate a single operator condition."""
-        if operator == 'eq':
-            return actual == expected
-        elif operator == 'neq':
-            return actual != expected
-        elif operator == 'contains':
-            return expected in actual
-        elif operator == 'not_contains':
-            return expected not in actual
-        elif operator == 'starts_with':
-            return actual.startswith(expected)
-        elif operator == 'regex':
-            try:
-                return bool(re.search(expected, actual))
-            except re.error:
-                return False
-        return False
-
     PIPELINE_DISCARD = '__discard__'
     PIPELINE_DISCARD_DISPLAY_NAME = 'Discarded'
 
+    def get_binding_info(self) -> tuple[str, str | None]:
+        """Get the binding type and UUID for this bot.
+
+        Returns:
+            tuple: (binding_type, binding_uuid) where binding_type is 'pipeline' or 'workflow'
+        """
+        binding_type = getattr(self.bot_entity, 'binding_type', 'pipeline') or 'pipeline'
+        binding_uuid = getattr(self.bot_entity, 'binding_uuid', None)
+
+        # Fallback to use_pipeline_uuid for backward compatibility
+        if not binding_uuid and binding_type == 'pipeline':
+            binding_uuid = self.bot_entity.use_pipeline_uuid
+
+        return binding_type, binding_uuid
+
     def resolve_pipeline_uuid(
         self,
         launcher_type: str,
@@ -84,56 +81,94 @@ class RuntimeBot:
         message_text: str,
         message_element_types: list[str] | None = None,
     ) -> tuple[str | None, bool]:
-        """Resolve pipeline UUID based on routing rules.
+        """Resolve pipeline UUID for message processing.
 
-        Rules are evaluated in order; first match wins.
+        NOTE: Routing rules have been removed. Bot now directly binds to a
-        Falls back to use_pipeline_uuid if no rule matches.
+        Pipeline or Workflow. This method is kept for backward compatibility
-
+        but only returns the direct binding.
-        Rule types:
-          - launcher_type: session type ("person" / "group")
-          - launcher_id: session / group id
-          - message_content: message text content
-          - message_has_element: message contains element of given type
-            (Image, Voice, File, Forward, Face, At, AtAll, Quote)
-            Operators: eq (has), neq (doesn't have)
-
-        Operators: eq, neq, contains, not_contains, starts_with, regex
-
-        When pipeline_uuid is ``__discard__``, the message should be
-        silently dropped by the caller.
 
         Returns:
-            tuple: (pipeline_uuid, routed_by_rule) - routed_by_rule is True
+            tuple: (pipeline_uuid, routed_by_rule) - routed_by_rule is always False
-            when a routing rule matched, False when falling back to default.
+            as routing rules are no longer used.
         """
-        rules = self.bot_entity.pipeline_routing_rules or []
+        binding_type, binding_uuid = self.get_binding_info()
-        element_type_set = set(message_element_types or [])
 
-        for rule in rules:
+        # If bound to workflow, return None for pipeline_uuid
-            rule_type = rule.get('type')
+        # The caller should check binding_type and handle accordingly
-            operator = rule.get('operator', 'eq')
+        if binding_type == 'workflow':
-            rule_value = rule.get('value', '')
+            # For workflow binding, we still need to return something
-            target_uuid = rule.get('pipeline_uuid')
+            # The actual workflow handling should be done by the caller
-            if not rule_type or not target_uuid:
+            return None, False
-                continue
 
-            if rule_type == 'launcher_type':
+        return binding_uuid, False
-                if self._match_operator(launcher_type, operator, rule_value):
-                    return target_uuid, True
-            elif rule_type == 'launcher_id':
-                if self._match_operator(str(launcher_id), operator, str(rule_value)):
-                    return target_uuid, True
-            elif rule_type == 'message_content':
-                if self._match_operator(message_text, operator, rule_value):
-                    return target_uuid, True
-            elif rule_type == 'message_has_element':
-                has_element = rule_value in element_type_set
-                if operator == 'eq' and has_element:
-                    return target_uuid, True
-                elif operator == 'neq' and not has_element:
-                    return target_uuid, True
 
-        return self.bot_entity.use_pipeline_uuid, False
+    async def _handle_workflow_message(
+        self,
+        event: platform_events.MessageEvent,
+        adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
+        workflow_uuid: str,
+        launcher_type: str,
+        launcher_id: str | int,
+        sender_id: str | int,
+    ) -> None:
+        """Handle message by executing the bound workflow directly."""
+        message_content = str(event.message_chain)
+        message_chain_obj = event.message_chain
+
+        # Build message context
+        sender_name = None
+        if hasattr(event, 'sender'):
+            sender = event.sender
+            if hasattr(sender, 'nickname'):
+                sender_name = sender.nickname
+            elif hasattr(sender, 'member_name'):
+                sender_name = sender.member_name
+
+        is_group = launcher_type == 'group'
+        message_context = {
+            'message_id': str(getattr(event, 'message_id', '')),
+            'message_content': message_content,
+            'sender_id': str(sender_id),
+            'sender_name': sender_name or 'User',
+            'platform': adapter.__class__.__name__,
+            'conversation_id': str(launcher_id),
+            'is_group': is_group,
+            'group_id': str(launcher_id) if is_group else None,
+            'mentions': [],
+            'reply_to': None,
+            'raw_message': {
+                'message': message_chain_obj.model_dump() if hasattr(message_chain_obj, 'model_dump') else str(message_chain_obj),
+                'launcher_id': launcher_id,
+                'session_type': launcher_type,
+            },
+        }
+
+        trigger_data = {
+            'message': message_content,
+            'message_chain': message_chain_obj.model_dump() if hasattr(message_chain_obj, 'model_dump') else str(message_chain_obj),
+            'session_type': launcher_type,
+            'connection_id': str(launcher_id),
+            'message_context': message_context,
+        }
+
+        session_id = f'{launcher_type}_{launcher_id}'
+        logger.info(f'Processing workflow message from {session_id}: {message_content}')
+
+        try:
+            from ..api.http.service.workflow import WorkflowExecutionFailedError
+
+            execution_id = await self.ap.workflow_service.execute_workflow(
+                workflow_uuid=workflow_uuid,
+                trigger_type='message',
+                trigger_data=trigger_data,
+                session_id=session_id,
+                user_id=str(sender_id),
+                bot_id=self.bot_entity.uuid,
+            )
+        except WorkflowExecutionFailedError as e:
+            await self.logger.error(f'Workflow execution failed: {e.message}')
+        except Exception as e:
+            await self.logger.error(f'Workflow execution error: {e}')
 
     async def _record_discarded_message(
         self,
@@ -229,6 +264,20 @@ class RuntimeBot:
 
                 message_text = str(event.message_chain)
                 element_types = [comp.type for comp in event.message_chain]
+                binding_type, binding_uuid = self.get_binding_info()
+
+                # Handle workflow binding separately from pipeline
+                if binding_type == 'workflow':
+                    await self._handle_workflow_message(
+                        event=event,
+                        adapter=adapter,
+                        workflow_uuid=binding_uuid,
+                        launcher_type='person',
+                        launcher_id=launcher_id,
+                        sender_id=event.sender.id,
+                    )
+                    return
+
                 pipeline_uuid, routed_by_rule = self.resolve_pipeline_uuid(
                     'person', launcher_id, message_text, element_types
                 )
@@ -290,6 +339,20 @@ class RuntimeBot:
 
                 message_text = str(event.message_chain)
                 element_types = [comp.type for comp in event.message_chain]
+                binding_type, binding_uuid = self.get_binding_info()
+
+                # Handle workflow binding separately from pipeline
+                if binding_type == 'workflow':
+                    await self._handle_workflow_message(
+                        event=event,
+                        adapter=adapter,
+                        workflow_uuid=binding_uuid,
+                        launcher_type='group',
+                        launcher_id=launcher_id,
+                        sender_id=event.sender.id,
+                    )
+                    return
+
                 pipeline_uuid, routed_by_rule = self.resolve_pipeline_uuid(
                     'group', launcher_id, message_text, element_types
                 )

src/langbot/pkg/platform/sources/aiocqhttp.py

@@ -4,6 +4,7 @@ import asyncio
 import traceback
 import datetime
 import json
+import time
 
 import aiocqhttp
 import pydantic
@@ -16,6 +17,37 @@ from ...utils import image
 import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_platform_logger
 
 
+_GROUP_NAME_CACHE_TTL_SECONDS = 3600
+_GROUP_NAME_NEGATIVE_CACHE_TTL_SECONDS = 60
+_GROUP_NAME_LOOKUP_TIMEOUT_SECONDS = 2
+_GROUP_MEMBER_INFO_CACHE_TTL_SECONDS = 86400
+_GROUP_MEMBER_INFO_NEGATIVE_CACHE_TTL_SECONDS = 600
+_GROUP_MEMBER_INFO_LOOKUP_TIMEOUT_SECONDS = 2
+
+
+def _normalize_base64_payload(value: str) -> str:
+    if value.startswith('base64://'):
+        return value.removeprefix('base64://')
+    if value.startswith('data:') and ';base64,' in value:
+        return value.split(';base64,', 1)[1]
+    return value
+
+
+def _get_field(data: dict, key: str, default: str = '') -> str:
+    value = data.get(key)
+    if value is None:
+        return default
+    return str(value)
+
+
+def _get_group_member_name(sender: dict) -> str:
+    return _get_field(sender, 'card') or _get_field(sender, 'nickname') or _get_field(sender, 'user_id')
+
+
+def _get_group_name_placeholder(group_id: typing.Union[int, str]) -> str:
+    return f'Group {group_id}'
+
+
 class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
     @staticmethod
     async def yiri2target(
@@ -35,7 +67,7 @@ class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConvert
             elif type(msg) is platform_message.Image:
                 arg = ''
                 if msg.base64:
-                    arg = msg.base64
+                    arg = _normalize_base64_payload(msg.base64)
                     msg_list.append(aiocqhttp.MessageSegment.image(f'base64://{arg}'))
                 elif msg.url:
                     arg = msg.url
@@ -50,7 +82,7 @@ class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConvert
             elif type(msg) is platform_message.Voice:
                 arg = ''
                 if msg.base64:
-                    arg = msg.base64
+                    arg = _normalize_base64_payload(msg.base64)
                     msg_list.append(aiocqhttp.MessageSegment.record(f'base64://{arg}'))
                 elif msg.url:
                     arg = msg.url
@@ -62,7 +94,10 @@ class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConvert
                 for node in msg.node_list:
                     msg_list.extend((await AiocqhttpMessageConverter.yiri2target(node.message_chain))[0])
             elif isinstance(msg, platform_message.File):
-                msg_list.append({'type': 'file', 'data': {'file': msg.url, 'name': msg.name}})
+                file = msg.url or msg.path
+                if not file and msg.base64:
+                    file = f'base64://{_normalize_base64_payload(msg.base64)}'
+                msg_list.append({'type': 'file', 'data': {'file': file, 'name': msg.name}})
             elif isinstance(msg, platform_message.Face):
                 if msg.face_type == 'face':
                     msg_list.append(aiocqhttp.MessageSegment.face(msg.face_id))
@@ -324,16 +359,96 @@ class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConvert
 
 
 class AiocqhttpEventConverter(abstract_platform_adapter.AbstractEventConverter):
+    def __init__(self):
+        self._group_name_cache: dict[typing.Union[int, str], tuple[str, float]] = {}
+        self._group_name_negative_cache: dict[typing.Union[int, str], float] = {}
+        self._group_member_info_cache: dict[
+            tuple[typing.Union[int, str], typing.Union[int, str]], tuple[dict, float]
+        ] = {}
+        self._group_member_info_negative_cache: dict[tuple[typing.Union[int, str], typing.Union[int, str]], float] = {}
+
     @staticmethod
     async def yiri2target(event: platform_events.MessageEvent, bot_account_id: int):
         return event.source_platform_object
 
-    @staticmethod
+    async def _get_group_name(self, group_id: typing.Union[int, str], bot=None) -> str:
-    async def target2yiri(event: aiocqhttp.Event, bot=None):
+        now = time.monotonic()
+        if group_id in self._group_name_cache:
+            group_name, expires_at = self._group_name_cache[group_id]
+            if expires_at > now:
+                return group_name
+            del self._group_name_cache[group_id]
+        if group_id in self._group_name_negative_cache:
+            expires_at = self._group_name_negative_cache[group_id]
+            if expires_at > now:
+                return ''
+            del self._group_name_negative_cache[group_id]
+        if bot is None:
+            return ''
+        try:
+            group_info = await asyncio.wait_for(
+                bot.get_group_info(group_id=group_id),
+                timeout=_GROUP_NAME_LOOKUP_TIMEOUT_SECONDS,
+            )
+        except Exception:
+            self._group_name_negative_cache[group_id] = now + _GROUP_NAME_NEGATIVE_CACHE_TTL_SECONDS
+            return ''
+        group_name = _get_field(group_info, 'group_name') if isinstance(group_info, dict) else ''
+        if group_name:
+            self._group_name_cache[group_id] = (group_name, now + _GROUP_NAME_CACHE_TTL_SECONDS)
+            self._group_name_negative_cache.pop(group_id, None)
+        else:
+            self._group_name_negative_cache[group_id] = now + _GROUP_NAME_NEGATIVE_CACHE_TTL_SECONDS
+        return group_name
+
+    async def _get_group_member_info(
+        self,
+        group_id: typing.Union[int, str],
+        user_id: typing.Union[int, str],
+        bot=None,
+    ) -> dict:
+        now = time.monotonic()
+        cache_key = (group_id, user_id)
+        if cache_key in self._group_member_info_cache:
+            member_info, expires_at = self._group_member_info_cache[cache_key]
+            if expires_at > now:
+                return member_info
+            del self._group_member_info_cache[cache_key]
+        if cache_key in self._group_member_info_negative_cache:
+            expires_at = self._group_member_info_negative_cache[cache_key]
+            if expires_at > now:
+                return {}
+            del self._group_member_info_negative_cache[cache_key]
+        if bot is None:
+            return {}
+        try:
+            member_info = await asyncio.wait_for(
+                bot.get_group_member_info(group_id=group_id, user_id=user_id),
+                timeout=_GROUP_MEMBER_INFO_LOOKUP_TIMEOUT_SECONDS,
+            )
+        except Exception:
+            self._group_member_info_negative_cache[cache_key] = now + _GROUP_MEMBER_INFO_NEGATIVE_CACHE_TTL_SECONDS
+            return {}
+        if isinstance(member_info, dict) and member_info:
+            self._group_member_info_cache[cache_key] = (
+                member_info,
+                now + _GROUP_MEMBER_INFO_CACHE_TTL_SECONDS,
+            )
+            self._group_member_info_negative_cache.pop(cache_key, None)
+            return member_info
+        self._group_member_info_negative_cache[cache_key] = now + _GROUP_MEMBER_INFO_NEGATIVE_CACHE_TTL_SECONDS
+        return {}
+
+    async def target2yiri(self, event: aiocqhttp.Event, bot=None):
         yiri_chain = await AiocqhttpMessageConverter.target2yiri(event.message, event.message_id, bot)
 
         if event.message_type == 'group':
             permission = 'MEMBER'
+            group_name = await self._get_group_name(event.group_id, bot) or _get_group_name_placeholder(event.group_id)
+            special_title = _get_field(event.sender, 'title')
+            if not special_title:
+                member_info = await self._get_group_member_info(event.group_id, event.sender['user_id'], bot)
+                special_title = _get_field(member_info, 'title')
 
             if 'role' in event.sender:
                 if event.sender['role'] == 'admin':
@@ -343,14 +458,14 @@ class AiocqhttpEventConverter(abstract_platform_adapter.AbstractEventConverter):
             converted_event = platform_events.GroupMessage(
                 sender=platform_entities.GroupMember(
                     id=event.sender['user_id'],  # message_seq 放哪？
-                    member_name=event.sender['nickname'],
+                    member_name=_get_group_member_name(event.sender),
                     permission=permission,
                     group=platform_entities.Group(
                         id=event.group_id,
-                        name=event.sender['nickname'],
+                        name=group_name,
                         permission=platform_entities.Permission.Member,
                     ),
-                    special_title=event.sender['title'] if 'title' in event.sender else '',
+                    special_title=special_title,
                 ),
                 message_chain=yiri_chain,
                 time=event.time,
@@ -374,7 +489,7 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
     bot: aiocqhttp.CQHttp = pydantic.Field(exclude=True, default_factory=aiocqhttp.CQHttp)
 
     message_converter: AiocqhttpMessageConverter = AiocqhttpMessageConverter()
-    event_converter: AiocqhttpEventConverter = AiocqhttpEventConverter()
+    event_converter: AiocqhttpEventConverter = pydantic.Field(default_factory=AiocqhttpEventConverter)
 
     on_websocket_connection_event_cache: typing.List[typing.Callable[[aiocqhttp.Event], None]] = []
 
@@ -433,9 +548,7 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
                     elif isinstance(component, platform_message.Image):
                         img_data = {}
                         if component.base64:
-                            b64 = component.base64
+                            b64 = _normalize_base64_payload(component.base64)
-                            if b64.startswith('data:'):
-                                b64 = b64.split(',', 1)[-1] if ',' in b64 else b64
                             img_data['file'] = f'base64://{b64}'
                         elif component.url:
                             img_data['file'] = component.url

src/langbot/pkg/platform/sources/websocket_adapter.py

@@ -422,6 +422,64 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
             session_type=session_type,
         )
 
+        # Determine if pipeline_uuid is a workflow or a legacy pipeline by querying both services
+        workflow_dict = await self.ap.workflow_service.get_workflow(pipeline_uuid)
+        pipeline_dict = await self.ap.pipeline_service.get_pipeline(pipeline_uuid)
+
+        if workflow_dict is not None:
+            # UUID exists in workflow table - execute as workflow
+            # Set pipeline_uuid for workflow nodes to broadcast messages correctly
+            self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid = pipeline_uuid
+
+            message_content = str(message_chain)
+            message_context = {
+                'message_id': str(message_id),
+                'message_content': message_content,
+                'sender_id': f'websocket_{connection.connection_id}',
+                'sender_name': 'User',
+                'platform': 'websocket',
+                'conversation_id': connection.connection_id,
+                'is_group': session_type == 'group',
+                'group_id': 'websocketgroup' if session_type == 'group' else None,
+                'mentions': [],
+                'reply_to': None,
+                'raw_message': {
+                    'message': message_chain_obj,
+                    'connection_id': connection.connection_id,
+                    'session_type': session_type,
+                },
+            }
+
+            trigger_data = {
+                'message': message_content,
+                'message_chain': message_chain_obj,
+                'session_type': session_type,
+                'connection_id': connection.connection_id,
+                'message_context': message_context,
+            }
+
+            try:
+                from ...api.http.service.workflow import WorkflowExecutionFailedError
+
+                # Log workflow execution start (matching pipeline logging)
+                session_id = f'{session_type}_{connection.connection_id}'
+                logger.info(f'Processing workflow message from {session_id}: {message_content}')
+
+                execution_id = await self.ap.workflow_service.execute_workflow(
+                    pipeline_uuid,  # This is actually a workflow UUID
+                    trigger_type='message',
+                    trigger_data=trigger_data,
+                    session_id=session_id,
+                    user_id=message_context['sender_id'],
+                    bot_id=self.ap.platform_mgr.websocket_proxy_bot.bot_entity.uuid,
+                )
+            except WorkflowExecutionFailedError as e:
+                await connection.send_queue.put({'type': 'error', 'message': e.message})
+            except Exception as e:
+                logger.error(f'Workflow websocket execution error: {e}', exc_info=True)
+                await connection.send_queue.put({'type': 'error', 'message': str(e)})
+            return
+
         # 添加消息源
         message_chain.insert(0, platform_message.Source(id=message_id, time=datetime.now().timestamp()))
 

src/langbot/pkg/plugin/connector.py

@@ -737,6 +737,8 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
         event_ctx = context.EventContext.from_event(event)
 
         if not self.is_enable_plugin:
+            event_ctx._emitted_plugins = []
+            event_ctx._response_sources = []
             return event_ctx
 
         # Pass include_plugins to runtime for filtering
@@ -745,9 +747,21 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
         )
 
         event_ctx = context.EventContext.model_validate(event_ctx_result['event_context'])
+        event_ctx._emitted_plugins = event_ctx_result.get('emitted_plugins', [])
+        if 'response_sources' in event_ctx_result:
+            event_ctx._response_sources = event_ctx_result['response_sources']
 
         return event_ctx
 
+    async def notify_plugin_diagnostic(self, diagnostic: dict[str, Any]) -> None:
+        """Best-effort diagnostic forwarding to the plugin runtime."""
+        if not self.is_enable_plugin:
+            return
+        try:
+            await self.handler.notify_plugin_diagnostic(diagnostic)
+        except Exception as e:
+            self.ap.logger.debug(f'Plugin diagnostic forwarding skipped: {e}')
+
     async def list_tools(self, bound_plugins: list[str] | None = None) -> list[ComponentManifest]:
         if not self.is_enable_plugin:
             return []

src/langbot/pkg/plugin/handler.py

@@ -26,6 +26,15 @@ from ..core import app
 from ..utils import constants
 
 
+class _RawAction:
+    def __init__(self, value: str):
+        self.value = value
+
+
+def _langbot_to_runtime_action(enum_name: str, fallback_value: str) -> Any:
+    return getattr(LangBotToRuntimeAction, enum_name, _RawAction(fallback_value))
+
+
 def _make_rag_error_response(error: Exception, error_type: str, **extra_context) -> handler.ActionResponse:
     """Create a clean error response for RAG operations.
 
@@ -354,9 +363,19 @@ class RuntimeConnectionHandler(handler.Handler):
                 extra_args=extra_args,
             )
 
+            # invoke_llm returns (message, usage_info) tuple
+            if isinstance(result, tuple) and len(result) == 2:
+                msg, usage_info = result
+                msg_dump = msg.model_dump()
+                # Attach usage info to message dump
+                if usage_info:
+                    msg_dump['usage'] = usage_info
+            else:
+                msg_dump = result.model_dump()
+
             return handler.ActionResponse.success(
                 data={
-                    'message': result.model_dump(),
+                    'message': msg_dump,
                 },
             )
 
@@ -923,6 +942,18 @@ class RuntimeConnectionHandler(handler.Handler):
 
         return result
 
+    async def notify_plugin_diagnostic(self, diagnostic: dict[str, Any]) -> dict[str, Any]:
+        """Notify the plugin runtime about a best-effort plugin diagnostic.
+
+        This intentionally uses the raw protocol string instead of a SDK enum so
+        LangBot can keep running with older langbot-plugin versions.
+        """
+        return await self.call_action(
+            _langbot_to_runtime_action('PLUGIN_DIAGNOSTIC', 'plugin_diagnostic'),
+            diagnostic,
+            timeout=5,
+        )
+
     async def list_tools(self, include_plugins: list[str] | None = None) -> list[dict[str, Any]]:
         """List tools"""
         result = await self.call_action(

src/langbot/pkg/provider/modelmgr/requester.py

@@ -81,8 +81,13 @@ class RuntimeProvider:
                 msg, usage_info = result
                 if usage_info:
                     _store_llm_usage(query, usage_info)
-                    input_tokens = usage_info.get('prompt_tokens', 0)
+                    input_tokens = usage_info.get('prompt_tokens', usage_info.get('input_tokens', 0))
-                    output_tokens = usage_info.get('completion_tokens', 0)
+                    output_tokens = usage_info.get('completion_tokens', usage_info.get('output_tokens', 0))
+                    # Attach usage info to message using object.__setattr__ to bypass pydantic validation
+                    try:
+                        object.__setattr__(msg, 'usage', usage_info)
+                    except (AttributeError, TypeError):
+                        pass  # If we can't set it, just skip it
                 return msg
             else:
                 return result
@@ -98,7 +103,7 @@ class RuntimeProvider:
 
                 # Import monitoring helper
                 try:
-                    from ...pipeline import monitoring_helper
+                    from ...pipeline import monitor
 
                     # Get monitoring metadata from query variables
                     if query.variables:
@@ -110,7 +115,7 @@ class RuntimeProvider:
                         pipeline_name = 'Unknown'
                         message_id = None
 
-                    await monitoring_helper.MonitoringHelper.record_llm_call(
+                    await monitor.MonitoringHelper.record_llm_call(
                         ap=self.requester.ap,
                         query=query,
                         bot_id=query.bot_uuid or 'unknown',
@@ -177,7 +182,7 @@ class RuntimeProvider:
 
                 # Import monitoring helper
                 try:
-                    from ...pipeline import monitoring_helper
+                    from ...pipeline import monitor
 
                     # Get monitoring metadata from query variables
                     if query.variables:
@@ -189,7 +194,7 @@ class RuntimeProvider:
                         pipeline_name = 'Unknown'
                         message_id = None
 
-                    await monitoring_helper.MonitoringHelper.record_llm_call(
+                    await monitor.MonitoringHelper.record_llm_call(
                         ap=self.requester.ap,
                         query=query,
                         bot_id=query.bot_uuid or 'unknown',

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

@@ -417,6 +417,30 @@ class LocalAgentRunner(runner.RequestRunner):
                     ce.text = final_user_message_text
                     break
 
+        mcp_loader = getattr(getattr(self.ap, 'tool_mgr', None), 'mcp_tool_loader', None)
+        if mcp_loader is not None:
+            resource_context = await mcp_loader.build_resource_context_for_query(query)
+            if resource_context:
+                resource_addition = (
+                    '\n\nMCP resource context selected by LangBot host:\n'
+                    f'{resource_context}\n\n'
+                    'Use this context as read-only reference material. If it conflicts with the user message, '
+                    'ask for clarification before taking external actions.'
+                )
+                if isinstance(user_message.content, str):
+                    user_message.content += resource_addition
+                elif isinstance(user_message.content, list):
+                    appended = False
+                    for ce in user_message.content:
+                        if ce.type == 'text':
+                            ce.text = (ce.text or '') + resource_addition
+                            appended = True
+                            break
+                    if not appended:
+                        user_message.content.append(
+                            provider_message.ContentElement.from_text(resource_addition.strip())
+                        )
+
         req_messages = self._build_request_messages(query, user_message)
 
         try:

src/langbot/pkg/provider/tools/loaders/plugin.py

@@ -33,6 +33,24 @@ class PluginToolLoader(loader.ToolLoader):
 
         return all_functions
 
+    async def get_tool_catalog(self, bound_plugins: list[str] | None = None) -> list[dict[str, typing.Any]]:
+        catalog: list[dict[str, typing.Any]] = []
+
+        for tool in await self.ap.plugin_connector.list_tools(bound_plugins):
+            catalog.append(
+                {
+                    'name': tool.metadata.name,
+                    'description': tool.spec['llm_prompt'],
+                    'human_desc': tool.metadata.description.en_US,
+                    'parameters': tool.spec['parameters'],
+                    'source': 'plugin',
+                    'source_name': tool.owner,
+                    'source_id': tool.owner,
+                }
+            )
+
+        return catalog
+
     async def has_tool(self, name: str) -> bool:
         """检查工具是否存在"""
         for tool in await self.ap.plugin_connector.list_tools():

src/langbot/pkg/provider/tools/toolmgr.py

@@ -59,6 +59,7 @@ class ToolManager:
         bound_plugins: list[str] | None = None,
         bound_mcp_servers: list[str] | None = None,
         include_skill_authoring: bool = False,
+        include_mcp_resource_tools: bool = True,
     ) -> list[resource_tool.LLMTool]:
         all_functions: list[resource_tool.LLMTool] = []
 
@@ -66,10 +67,51 @@ class ToolManager:
         if include_skill_authoring:
             all_functions.extend(await self.skill_tool_loader.get_tools())
         all_functions.extend(await self.plugin_tool_loader.get_tools(bound_plugins))
-        all_functions.extend(await self.mcp_tool_loader.get_tools(bound_mcp_servers))
+        all_functions.extend(
+            await self.mcp_tool_loader.get_tools(
+                bound_mcp_servers,
+                include_resource_tools=include_mcp_resource_tools,
+            )
+        )
 
         return all_functions
 
+    async def get_tool_catalog(
+        self,
+        bound_plugins: list[str] | None = None,
+        bound_mcp_servers: list[str] | None = None,
+        include_skill_authoring: bool = False,
+        include_mcp_resource_tools: bool = False,
+    ) -> list[dict[str, typing.Any]]:
+        catalog: list[dict[str, typing.Any]] = []
+
+        def append_tools(source: str, source_name: str, tools: list[resource_tool.LLMTool]) -> None:
+            for tool in tools:
+                catalog.append(
+                    {
+                        'name': tool.name,
+                        'description': tool.description,
+                        'human_desc': tool.human_desc,
+                        'parameters': tool.parameters,
+                        'source': source,
+                        'source_name': source_name,
+                    }
+                )
+
+        append_tools('builtin', 'LangBot', await self.native_tool_loader.get_tools())
+        if include_skill_authoring:
+            append_tools('skill', 'LangBot', await self.skill_tool_loader.get_tools())
+        catalog.extend(await self.plugin_tool_loader.get_tool_catalog(bound_plugins))
+
+        if self.mcp_tool_loader:
+            for item in await self.mcp_tool_loader.get_tool_catalog(
+                bound_mcp_servers,
+                include_resource_tools=include_mcp_resource_tools,
+            ):
+                catalog.append(item)
+
+        return catalog
+
     async def get_tool_by_name(self, name: str) -> tool_loader.ToolLookupResult | None:
         """Get tool by name from any active loader."""
         for active_loader in (

src/langbot/pkg/workflow/adapters.py

@@ -0,0 +1,204 @@
+"""Workflow-Pipeline通信适配器
+
+这个模块提供了Workflow和Pipeline之间的通信适配，使用SDK标准的MessageEnvelope格式。
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class _WorkflowPipelineCaptureAdapter:
+    """Workflow-Pipeline通信适配器
+    
+    用于在Workflow节点和Pipeline之间进行标准化的消息传递。
+    支持MessageEnvelope格式的双向转换。
+    """
+    
+    def __init__(self, context: Any):
+        """初始化适配器
+        
+        Args:
+            context: ExecutionContext - Workflow执行上下文
+        """
+        self.context = context
+        self.responses: list[dict[str, Any]] = []
+        self.bot_account_id: Optional[str] = None
+        self._logger = logging.getLogger(__name__)
+    
+    async def call_pipeline_with_envelope(
+        self,
+        envelope: Any,
+        pipeline_executor: Any
+    ) -> Any:
+        """使用MessageEnvelope调用Pipeline
+        
+        Args:
+            envelope: MessageEnvelope - 标准消息信封
+            pipeline_executor: Pipeline执行器实例
+            
+        Returns:
+            MessageEnvelope - 执行结果信封
+        """
+        try:
+            # 动态导入以避免循环依赖
+            from langbot_plugin_sdk.workflow import envelope_to_query, query_to_envelope
+            
+            # 1. 转换为Query
+            query = envelope_to_query(envelope)
+            
+            # 2. 调用Pipeline
+            result_query = await pipeline_executor.execute(query)
+            
+            # 3. 转换回Envelope
+            result_envelope = query_to_envelope(result_query, envelope)
+            
+            self._logger.debug(
+                f'Pipeline execution completed for workflow {envelope.workflow_id}',
+                extra={
+                    'workflow_id': envelope.workflow_id,
+                    'execution_id': envelope.execution_id,
+                    'node_id': envelope.node_id,
+                }
+            )
+            
+            return result_envelope
+            
+        except Exception as e:
+            self._logger.error(
+                f'Pipeline execution failed: {e}',
+                exc_info=True,
+                extra={
+                    'workflow_id': envelope.workflow_id,
+                    'execution_id': envelope.execution_id,
+                    'node_id': envelope.node_id,
+                }
+            )
+            raise
+    
+    def validate_envelope(self, envelope: Any) -> bool:
+        """验证MessageEnvelope的有效性
+        
+        Args:
+            envelope: MessageEnvelope - 要验证的消息信封
+            
+        Returns:
+            bool - 验证是否通过
+        """
+        required_fields = [
+            'message_id',
+            'workflow_id',
+            'node_id',
+            'execution_id',
+            'payload',
+            'launcher_type',
+        ]
+        
+        for field in required_fields:
+            if not hasattr(envelope, field):
+                self._logger.warning(
+                    f'MessageEnvelope missing required field: {field}'
+                )
+                return False
+        
+        return True
+    
+    def get_responses(self) -> list[dict[str, Any]]:
+        """获取所有响应
+        
+        Returns:
+            list - 响应列表
+        """
+        return self.responses.copy()
+    
+    def add_response(self, response: dict[str, Any]) -> None:
+        """添加响应
+        
+        Args:
+            response: dict - 响应数据
+        """
+        self.responses.append(response)
+    
+    def get_last_text_response(self) -> str:
+        """获取最后一个文本响应
+        
+        Returns:
+            str - 最后一个响应的文本内容
+        """
+        if not self.responses:
+            return ''
+        
+        last_response = self.responses[-1]
+        return str(last_response.get('content', '') or '')
+    
+    def clear_responses(self) -> None:
+        """清空所有响应"""
+        self.responses.clear()
+
+
+class WorkflowPipelineCompatibilityLayer:
+    """Workflow-Pipeline兼容性层
+    
+    提供向后兼容性，支持旧的Pipeline Query格式和新的MessageEnvelope格式。
+    """
+    
+    def __init__(self):
+        """初始化兼容性层"""
+        self._logger = logging.getLogger(__name__)
+    
+    def is_workflow_context(self, query: Any) -> bool:
+        """检查Query是否包含Workflow上下文
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            bool - 是否来自Workflow
+        """
+        if hasattr(query, 'is_from_workflow'):
+            return query.is_from_workflow()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return bool(context and context.get('workflow_id'))
+        
+        return False
+    
+    def get_workflow_id(self, query: Any) -> Optional[str]:
+        """从Query获取Workflow ID
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            str - Workflow ID，如果不存在则返回None
+        """
+        if hasattr(query, 'get_workflow_id'):
+            return query.get_workflow_id()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return context.get('workflow_id') if context else None
+        
+        return None
+    
+    def get_execution_id(self, query: Any) -> Optional[str]:
+        """从Query获取执行ID
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            str - 执行ID，如果不存在则返回None
+        """
+        if hasattr(query, 'get_execution_id'):
+            return query.get_execution_id()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return context.get('execution_id') if context else None
+        
+        return None

src/langbot/pkg/workflow/debug.py

@@ -0,0 +1,504 @@
+"""Workflow debug execution support.
+
+This module provides debugging capabilities for workflow execution, including:
+- ExecutionLog: Structured log entries for execution tracking
+- DebugExecutionState: State management for debug sessions (pause, resume, breakpoints)
+- DebugWorkflowExecutor: Extended executor with step-by-step debugging support
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import traceback
+import uuid
+from datetime import datetime
+from typing import Any, Optional, TYPE_CHECKING
+
+from .entities import (
+    WorkflowDefinition,
+    NodeDefinition,
+    EdgeDefinition,
+    ExecutionContext,
+    ExecutionStatus,
+    NodeState,
+    NodeStatus,
+)
+from .executor import WorkflowExecutor
+
+if TYPE_CHECKING:
+    from ..core import app
+
+logger = logging.getLogger(__name__)
+
+
+class ExecutionLog:
+    """Execution log entry"""
+
+    def __init__(self, level: str, message: str, node_id: Optional[str] = None, data: Optional[dict] = None):
+        self.id = str(uuid.uuid4())
+        self.timestamp = datetime.now().isoformat()
+        self.level = level
+        self.message = message
+        self.node_id = node_id
+        self.data = data or {}
+
+    def to_dict(self) -> dict:
+        return {
+            'id': self.id,
+            'timestamp': self.timestamp,
+            'level': self.level,
+            'message': self.message,
+            'node_id': self.node_id,
+            'data': self.data,
+        }
+
+
+class DebugExecutionState:
+    """State for a debug execution"""
+
+    def __init__(self, execution_id: str, breakpoints: list[str] = None):
+        self.execution_id = execution_id
+        self.status: str = 'running'
+        self.is_paused: bool = False
+        self.is_stopped: bool = False
+        self.current_node_id: Optional[str] = None
+        self.breakpoints: set[str] = set(breakpoints or [])
+        self.logs: list[ExecutionLog] = []
+        self.pending_logs: list[ExecutionLog] = []
+        self._pause_event = asyncio.Event()
+        self._pause_event.set()  # Initially not paused
+        self._stop_event = asyncio.Event()
+
+    def add_log(self, level: str, message: str, node_id: str = None, data: dict = None):
+        """Add a log entry"""
+        log = ExecutionLog(level, message, node_id, data)
+        self.logs.append(log)
+        self.pending_logs.append(log)
+        logger.log(
+            getattr(logging, level.upper(), logging.INFO),
+            f'[Workflow Debug] {message}',
+            extra={'node_id': node_id, 'data': data},
+        )
+
+    def get_pending_logs(self) -> list[dict]:
+        """Get and clear pending logs"""
+        logs = [log.to_dict() for log in self.pending_logs]
+        self.pending_logs = []
+        return logs
+
+    def pause(self):
+        """Pause execution"""
+        self.is_paused = True
+        self._pause_event.clear()
+        self.add_log('info', 'Execution paused')
+
+    def resume(self):
+        """Resume execution"""
+        self.is_paused = False
+        self._pause_event.set()
+        self.add_log('info', 'Execution resumed')
+
+    def stop(self):
+        """Stop execution"""
+        self.is_stopped = True
+        self.status = 'cancelled'
+        self._stop_event.set()
+        self._pause_event.set()  # Release any pause
+        self.add_log('info', 'Execution stopped')
+
+    async def wait_if_paused(self):
+        """Wait if execution is paused"""
+        if self.is_paused:
+            self.add_log('info', 'Waiting for resume...')
+            await self._pause_event.wait()
+
+    def check_breakpoint(self, node_id: str) -> bool:
+        """Check if there's a breakpoint at the given node"""
+        return node_id in self.breakpoints
+
+
+class DebugWorkflowExecutor(WorkflowExecutor):
+    """
+    Debug-enabled workflow executor with step-by-step execution support.
+    Extends WorkflowExecutor with debugging capabilities.
+    """
+
+    # Class-level storage for active debug sessions
+    _debug_states: dict[str, DebugExecutionState] = {}
+
+    def __init__(self, ap: Optional['app.Application'] = None):
+        super().__init__(ap)
+
+    @classmethod
+    def get_debug_state(cls, execution_id: str) -> Optional[DebugExecutionState]:
+        """Get debug state for an execution"""
+        return cls._debug_states.get(execution_id)
+
+    @classmethod
+    def create_debug_state(cls, execution_id: str, breakpoints: list[str] = None) -> DebugExecutionState:
+        """Create a new debug state"""
+        state = DebugExecutionState(execution_id, breakpoints)
+        cls._debug_states[execution_id] = state
+        return state
+
+    @classmethod
+    def remove_debug_state(cls, execution_id: str):
+        """Remove debug state for an execution"""
+        cls._debug_states.pop(execution_id, None)
+
+    async def execute_debug(
+        self,
+        workflow: WorkflowDefinition,
+        context: ExecutionContext,
+        debug_state: DebugExecutionState,
+    ) -> ExecutionContext:
+        """
+        Execute a workflow in debug mode.
+
+        Args:
+            workflow: Workflow definition
+            context: Execution context
+            debug_state: Debug execution state
+
+        Returns:
+            Updated execution context
+        """
+        context.status = ExecutionStatus.RUNNING
+        context.start_time = datetime.now()
+        debug_state.add_log('info', f'Starting debug execution for workflow: {workflow.name}')
+
+        try:
+            # Build execution graph
+            node_map = {node.id: node for node in workflow.nodes}
+            edge_map = self._build_edge_map(workflow.edges)
+            self._edges = workflow.edges
+
+            # Initialize node states
+            for node in workflow.nodes:
+                if node.id not in context.node_states:
+                    context.node_states[node.id] = NodeState(node_id=node.id)
+
+            # Find start node(s)
+            start_nodes = self._find_start_nodes(workflow.nodes, workflow.edges)
+
+            if not start_nodes:
+                raise ValueError('No start nodes found in workflow')
+
+            debug_state.add_log('info', f'Found {len(start_nodes)} start node(s)')
+
+            # Execute from start nodes
+            for start_node in start_nodes:
+                if debug_state.is_stopped:
+                    break
+
+                await self._execute_debug_from_node(
+                    start_node, node_map, edge_map, context, debug_state, workflow.settings.max_retries
+                )
+
+            # Set final status
+            if debug_state.is_stopped:
+                context.status = ExecutionStatus.CANCELLED
+                debug_state.status = 'cancelled'
+            else:
+                all_completed = all(
+                    state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED) for state in context.node_states.values()
+                )
+
+                if all_completed:
+                    context.status = ExecutionStatus.COMPLETED
+                    debug_state.status = 'completed'
+                    debug_state.add_log('info', 'Workflow execution completed successfully')
+                else:
+                    has_failed = any(state.status == NodeStatus.FAILED for state in context.node_states.values())
+                    if has_failed:
+                        context.status = ExecutionStatus.FAILED
+                        debug_state.status = 'error'
+
+        except Exception as e:
+            context.status = ExecutionStatus.FAILED
+            context.error = str(e)
+            debug_state.status = 'error'
+            debug_state.add_log('error', f'Workflow execution failed: {e}', data={'traceback': traceback.format_exc()})
+            logger.error(f'Debug workflow execution failed: {e}\n{traceback.format_exc()}')
+
+        finally:
+            context.end_time = datetime.now()
+
+        return context
+
+    async def _execute_debug_from_node(
+        self,
+        node: NodeDefinition,
+        node_map: dict[str, NodeDefinition],
+        edge_map: dict[str, list[EdgeDefinition]],
+        context: ExecutionContext,
+        debug_state: DebugExecutionState,
+        max_retries: int = 3,
+    ):
+        """Execute workflow from a node with debug support"""
+
+        # Check if stopped
+        if debug_state.is_stopped:
+            return
+
+        # Wait if paused
+        await debug_state.wait_if_paused()
+
+        # Check if should skip
+        if await self._should_skip_node(node, context):
+            if context.node_states[node.id].status == NodeStatus.SKIPPED:
+                debug_state.add_log('info', f'Skipping node: {node.id}', node_id=node.id)
+            return
+
+        # Check breakpoint
+        if debug_state.check_breakpoint(node.id):
+            debug_state.add_log('info', f'Hit breakpoint at node: {node.id}', node_id=node.id)
+            debug_state.pause()
+            await debug_state.wait_if_paused()
+
+        # Update current node
+        debug_state.current_node_id = node.id
+        debug_state.add_log('info', f'Executing node: {node.id} ({node.type})', node_id=node.id)
+
+        # Execute node
+        await self._execute_debug_node(node, context, debug_state, max_retries)
+
+        # Check if stopped or failed
+        if debug_state.is_stopped:
+            return
+        if context.node_states[node.id].status == NodeStatus.FAILED:
+            return
+
+        # Get outgoing edges
+        outgoing_edges = edge_map.get(node.id, [])
+
+        # Execute next nodes
+        for edge in outgoing_edges:
+            if debug_state.is_stopped:
+                break
+
+            target_node = node_map.get(edge.target_node)
+            if not target_node:
+                continue
+
+            # Check edge condition
+            if edge.condition:
+                condition_met = await self._evaluate_condition(edge.condition, context)
+                if not condition_met:
+                    debug_state.add_log('debug', f'Edge condition not met: {edge.condition}', node_id=node.id)
+                    continue
+
+            # Check if all inputs are ready
+            if await self._inputs_ready(target_node, edge_map, context):
+                await self._execute_debug_from_node(target_node, node_map, edge_map, context, debug_state, max_retries)
+
+    async def _execute_debug_node(
+        self, node: NodeDefinition, context: ExecutionContext, debug_state: DebugExecutionState, max_retries: int = 3
+    ):
+        """Execute a single node with debug logging"""
+
+        node_state = context.node_states[node.id]
+        node_state.status = NodeStatus.RUNNING
+        node_state.start_time = datetime.now()
+
+        # Get node instance (pass ap for access to services)
+        node_instance = self.registry.create_instance(node.type, node.id, node.config, ap=self.ap)
+
+        if not node_instance:
+            node_state.status = NodeStatus.FAILED
+            node_state.error = f'Unknown node type: {node.type}'
+            node_state.end_time = datetime.now()
+            debug_state.add_log('error', f'Unknown node type: {node.type}', node_id=node.id)
+            self._record_execution_step(node, node_state, context)
+            await self._persist_node_execution(node, node_state, context)
+            return
+
+        # Resolve inputs
+        inputs = await self._resolve_inputs(node, context)
+        node_state.inputs = inputs
+        debug_state.add_log(
+            'debug', 'Node inputs resolved', node_id=node.id, data={'inputs': self._safe_serialize(inputs)}
+        )
+
+        # Validate inputs
+        validation_errors = await node_instance.validate_inputs(inputs)
+        if validation_errors:
+            node_state.status = NodeStatus.FAILED
+            node_state.error = '; '.join(validation_errors)
+            node_state.end_time = datetime.now()
+            debug_state.add_log('error', f'Input validation failed: {node_state.error}', node_id=node.id)
+            self._record_execution_step(node, node_state, context)
+            await self._persist_node_execution(node, node_state, context)
+            return
+
+        # Execute with retries
+        for attempt in range(max_retries + 1):
+            if debug_state.is_stopped:
+                node_state.status = NodeStatus.FAILED
+                node_state.error = 'Execution stopped'
+                node_state.end_time = datetime.now()
+                break
+
+            try:
+                outputs = await node_instance.execute(inputs, context)
+                node_state.outputs = outputs
+                node_state.status = NodeStatus.COMPLETED
+                node_state.end_time = datetime.now()
+
+                duration_ms = int((node_state.end_time - node_state.start_time).total_seconds() * 1000)
+                debug_state.add_log(
+                    'info',
+                    f'Node completed in {duration_ms}ms',
+                    node_id=node.id,
+                    data={'outputs': self._safe_serialize(outputs), 'duration_ms': duration_ms},
+                )
+                break
+
+            except Exception as e:
+                node_state.retry_count = attempt + 1
+                debug_state.add_log(
+                    'warning', f'Node execution failed (attempt {attempt + 1}/{max_retries + 1}): {e}', node_id=node.id
+                )
+
+                if attempt < max_retries:
+                    await asyncio.sleep(1)
+                else:
+                    node_state.status = NodeStatus.FAILED
+                    node_state.error = str(e)
+                    node_state.end_time = datetime.now()
+                    debug_state.add_log(
+                        'error',
+                        f'Node failed after {max_retries + 1} attempts: {e}',
+                        node_id=node.id,
+                        data={'error': str(e), 'traceback': traceback.format_exc()},
+                    )
+
+        self._record_execution_step(node, node_state, context)
+        await self._persist_node_execution(node, node_state, context)
+
+    async def step_execute(
+        self,
+        workflow: WorkflowDefinition,
+        context: ExecutionContext,
+        debug_state: DebugExecutionState,
+    ) -> dict:
+        """
+        Execute one step (one node) in debug mode.
+
+        Returns:
+            Dict with node_id, node_state, and completed status
+        """
+        # Find next node to execute
+        next_node = self._find_next_executable_node(workflow, context)
+
+        if not next_node:
+            debug_state.status = 'completed'
+            return {'completed': True}
+
+        # Execute single node
+        debug_state.current_node_id = next_node.id
+        await self._execute_debug_node(next_node, context, debug_state, workflow.settings.max_retries)
+
+        node_state = context.node_states.get(next_node.id)
+
+        # Check if workflow is complete
+        all_done = all(
+            state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED, NodeStatus.FAILED)
+            for state in context.node_states.values()
+        )
+
+        if all_done:
+            debug_state.status = 'completed'
+            context.status = ExecutionStatus.COMPLETED
+
+        return {
+            'node_id': next_node.id,
+            'node_state': {
+                'status': node_state.status.value if node_state else 'unknown',
+                'inputs': self._safe_serialize(node_state.inputs) if node_state else {},
+                'outputs': self._safe_serialize(node_state.outputs) if node_state else {},
+                'error': node_state.error if node_state else None,
+            },
+            'completed': all_done,
+        }
+
+    def _find_next_executable_node(
+        self, workflow: WorkflowDefinition, context: ExecutionContext
+    ) -> Optional[NodeDefinition]:
+        """Find the next node that can be executed"""
+        edge_map = self._build_edge_map(workflow.edges)
+
+        for node in workflow.nodes:
+            state = context.node_states.get(node.id)
+
+            # Skip completed, running, or failed nodes
+            if state and state.status in (
+                NodeStatus.COMPLETED,
+                NodeStatus.RUNNING,
+                NodeStatus.FAILED,
+                NodeStatus.SKIPPED,
+            ):
+                continue
+
+            incoming_nodes = self._incoming_dependency_nodes(node.id, edge_map)
+
+            # If no incoming nodes, it's a start node
+            if not incoming_nodes:
+                return node
+
+            # Check if all incoming nodes are done
+            all_incoming_done = True
+            for source_id in incoming_nodes:
+                source_state = context.node_states.get(source_id)
+                if not source_state or source_state.status not in (NodeStatus.COMPLETED, NodeStatus.SKIPPED):
+                    all_incoming_done = False
+                    break
+
+            if all_incoming_done:
+                return node
+
+        return None
+
+    def _safe_serialize(self, data: Any) -> Any:
+        """Safely serialize data for logging"""
+        if data is None:
+            return None
+        if isinstance(data, (str, int, float, bool)):
+            return data
+        if isinstance(data, (list, tuple)):
+            return [self._safe_serialize(item) for item in data[:100]]  # Limit list size
+        if isinstance(data, dict):
+            result = {}
+            for key, value in list(data.items())[:50]:  # Limit dict size
+                result[str(key)] = self._safe_serialize(value)
+            return result
+        # For complex objects, try to convert to string
+        try:
+            return str(data)[:1000]  # Limit string length
+        except Exception:
+            return '<non-serializable>'
+
+    def get_execution_state(self, context: ExecutionContext, debug_state: DebugExecutionState) -> dict:
+        """Get current execution state for API response"""
+        node_states = {}
+        for node_id, state in context.node_states.items():
+            node_states[node_id] = {
+                'status': state.status.value,
+                'inputs': self._safe_serialize(state.inputs),
+                'outputs': self._safe_serialize(state.outputs),
+                'error': state.error,
+                'startTime': state.start_time.isoformat() if state.start_time else None,
+                'endTime': state.end_time.isoformat() if state.end_time else None,
+                'duration': int((state.end_time - state.start_time).total_seconds() * 1000)
+                if state.start_time and state.end_time
+                else None,
+            }
+
+        return {
+            'status': debug_state.status,
+            'current_node_id': debug_state.current_node_id,
+            'node_states': node_states,
+            'new_logs': debug_state.get_pending_logs(),
+            'error': context.error,
+        }

src/langbot/pkg/workflow/entities.py

@@ -0,0 +1,168 @@
+"""Workflow entities and data models
+
+This module defines workflow entities using SDK standard entities where available,
+and local-specific entities for LangBot_copy-specific functionality.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Optional
+import pydantic
+
+# Import SDK entities for standard workflow protocol types
+# These are re-exported for use by other modules in the workflow package.
+from langbot_plugin.api.entities.builtin.workflow.entities import (
+    ExecutionContext as ExecutionContext,
+    ExecutionStep as ExecutionStep,
+    MessageContext as MessageContext,
+    NodeDefinition,
+    NodeState as NodeState,
+    PortDefinition as PortDefinition,
+)
+from langbot_plugin.api.entities.builtin.workflow.enums import (
+    ExecutionStatus as ExecutionStatus,
+    NodeStatus as NodeStatus,
+)
+
+__all__ = [
+    "ExecutionContext",
+    "ExecutionStep",
+    "MessageContext",
+    "NodeDefinition",
+    "NodeState",
+    "PortDefinition",
+    "ExecutionStatus",
+    "NodeStatus",
+]
+
+
+class Position(pydantic.BaseModel):
+    """Node position on canvas"""
+
+    x: float = 0
+    y: float = 0
+
+
+class EdgeDefinition(pydantic.BaseModel):
+    """Workflow edge definition (connection between nodes)"""
+
+    id: str
+    source_node: str
+    source_port: str = 'output'
+    target_node: str
+    target_port: str = 'input'
+    edge_type: str = 'legacy'  # control, data, or legacy (old mixed semantics)
+    condition: Optional[str] = None  # Optional condition expression
+
+
+class TriggerDefinition(pydantic.BaseModel):
+    """Workflow trigger definition"""
+
+    id: str
+    type: str  # message, cron, event, webhook
+    config: dict[str, Any] = {}
+    enabled: bool = True
+
+
+class WorkflowSettings(pydantic.BaseModel):
+    """Workflow settings"""
+
+    # Execution settings
+    max_execution_time: int = 300  # seconds
+    max_retries: int = 3
+    retry_delay: int = 5  # seconds
+
+    # Error handling
+    error_handling: str = 'stop'  # stop, continue, retry
+
+    # Logging
+    log_level: str = 'info'
+    save_execution_history: bool = True
+
+    # Concurrency
+    max_concurrent_executions: int = 10
+
+
+class SafetyConfig(pydantic.BaseModel):
+    """Safety configuration (inherited from Pipeline)"""
+
+    content_filter: dict[str, Any] = {'enable': False, 'sensitive_words': [], 'replace_with': '***'}
+    rate_limit: dict[str, Any] = {'enable': False, 'requests_per_minute': 60, 'burst_limit': 10}
+
+
+class OutputConfig(pydantic.BaseModel):
+    """Output configuration (inherited from Pipeline)"""
+
+    long_text_processing: dict[str, Any] = {
+        'strategy': 'split',  # split, truncate, file
+        'max_length': 4000,
+        'split_separator': '\n\n',
+    }
+    force_delay: dict[str, Any] = {'enable': False, 'min_delay_ms': 0, 'max_delay_ms': 0}
+    misc: dict[str, Any] = {}
+
+
+class WorkflowGlobalConfig(pydantic.BaseModel):
+    """Workflow global configuration (inherited from Pipeline capabilities)"""
+
+    safety: SafetyConfig = SafetyConfig()
+    output: OutputConfig = OutputConfig()
+
+
+class ExtensionsPreferences(pydantic.BaseModel):
+    """Extensions preferences (same as Pipeline)"""
+
+    enable_all_plugins: bool = True
+    enable_all_mcp_servers: bool = True
+    plugins: list[str] = []
+    mcp_servers: list[str] = []
+
+
+class ConversationVariable(pydantic.BaseModel):
+    """Conversation-level variable definition"""
+
+    name: str
+    type: str = 'string'  # string, number, boolean, object, array
+    description: str = ''
+    default_value: Any = None
+    max_length: Optional[int] = None  # For strings
+
+
+class WorkflowDefinition(pydantic.BaseModel):
+    """Complete workflow definition"""
+
+    uuid: str
+    name: str
+    description: str = ''
+    emoji: str = '💼'
+    version: int = 1
+
+    # Workflow graph
+    nodes: list[NodeDefinition] = []
+    edges: list[EdgeDefinition] = []
+
+    # Variables
+    variables: dict[str, Any] = {}  # Global variables
+    conversation_variables: list[ConversationVariable] = []  # Session-level variables
+
+    # Settings
+    settings: WorkflowSettings = WorkflowSettings()
+
+    # Triggers (for automation)
+    triggers: list[TriggerDefinition] = []
+
+    # Global configuration (inherited from Pipeline)
+    global_config: WorkflowGlobalConfig = WorkflowGlobalConfig()
+
+    # Extensions
+    extensions_preferences: ExtensionsPreferences = ExtensionsPreferences()
+
+    # Metadata
+    is_enabled: bool = True
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    # Source tracking (for imported workflows)
+    source: Optional[str] = None  # dify, n8n, langflow, etc.
+    source_id: Optional[str] = None

src/langbot/pkg/workflow/executor.py

@@ -0,0 +1,759 @@
+"""Workflow execution engine.
+
+This module contains the core workflow execution logic:
+- WorkflowExecutor: Main execution engine with control flow handling
+- ParallelExecutor: Parallel branch execution
+- LoopExecutor: Loop/iterator execution
+
+Debug execution support has been moved to the ``debug`` module.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+import uuid
+from datetime import datetime
+from typing import Any, Optional, TYPE_CHECKING
+
+import sqlalchemy
+
+from .entities import (
+    WorkflowDefinition,
+    NodeDefinition,
+    EdgeDefinition,
+    ExecutionContext,
+    ExecutionStatus,
+    NodeState,
+    NodeStatus,
+    ExecutionStep,
+)
+from ..entity.persistence import workflow as persistence_workflow
+from .registry import NodeTypeRegistry
+from .safe_eval import safe_eval_with_vars
+
+if TYPE_CHECKING:
+    from ..core import app
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowExecutor:
+    """
+    Workflow execution engine.
+    Handles the execution of workflow definitions with proper control flow.
+    """
+
+    def __init__(self, ap: Optional['app.Application'] = None):
+        self.ap = ap
+        self.registry = NodeTypeRegistry.instance()
+        self._edges: list[EdgeDefinition] = []
+
+    async def execute(
+        self, workflow: WorkflowDefinition, context: ExecutionContext, start_node_id: Optional[str] = None
+    ) -> ExecutionContext:
+        """
+        Execute a workflow.
+
+        Args:
+            workflow: Workflow definition
+            context: Execution context
+            start_node_id: Optional starting node (for resumption)
+
+        Returns:
+            Updated execution context
+        """
+        context.status = ExecutionStatus.RUNNING
+        context.start_time = datetime.now()
+
+        try:
+            # Build execution graph
+            node_map = {node.id: node for node in workflow.nodes}
+            edge_map = self._build_edge_map(workflow.edges)
+            self._edges = workflow.edges
+
+            # Initialize node states
+            for node in workflow.nodes:
+                if node.id not in context.node_states:
+                    context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
+
+            # Find start node(s)
+            if start_node_id:
+                start_nodes = [node_map[start_node_id]]
+            else:
+                start_nodes = self._find_start_nodes(workflow.nodes, workflow.edges)
+
+            if not start_nodes:
+                raise ValueError('No start nodes found in workflow')
+
+            # Execute from start nodes
+            for start_node in start_nodes:
+                await self._execute_from_node(
+                    start_node, node_map, edge_map, context, workflow.settings.max_retries, path=set()
+                )
+
+            # Check final status
+            all_completed = all(
+                state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED) for state in context.node_states.values()
+            )
+
+            if all_completed:
+                context.status = ExecutionStatus.COMPLETED
+            else:
+                # Some nodes might still be waiting
+                has_failed = any(state.status == NodeStatus.FAILED for state in context.node_states.values())
+                if has_failed:
+                    context.status = ExecutionStatus.FAILED
+
+        except Exception as e:
+            context.status = ExecutionStatus.FAILED
+            context.error = str(e)
+            logger.error(
+                'Workflow execution failed',
+                exc_info=True,
+                extra={
+                    'workflow_id': workflow.uuid,
+                    'execution_id': context.execution_id,
+                    'node_states': {
+                        node_id: {
+                            'status': state.status.value if state.status else None,
+                            'error': state.error,
+                        }
+                        for node_id, state in context.node_states.items()
+                    },
+                },
+            )
+
+            # Note: Frontend panel logging has been removed.
+            # A new solution will be implemented separately.
+
+        finally:
+            context.end_time = datetime.now()
+
+            # Note: Frontend panel logging has been removed.
+            # A new solution will be implemented separately.
+
+        return context
+
+    async def _execute_from_node(
+        self,
+        node: NodeDefinition,
+        node_map: dict[str, NodeDefinition],
+        edge_map: dict[str, list[EdgeDefinition]],
+        context: ExecutionContext,
+        max_retries: int = 3,
+        path: set[str] | None = None,
+    ):
+        """Execute workflow starting from a specific node"""
+
+        # Initialize path set for cycle detection (path-based, not global visited)
+        if path is None:
+            path = set()
+
+        # Check for circular dependency on the *current path* only
+        # This correctly allows diamond shapes (A→B, A→C, B→D, C→D)
+        if node.id in path:
+            logger.warning(f'Circular dependency detected at node: {node.id}')
+            context.node_states[node.id].status = NodeStatus.SKIPPED
+            context.node_states[node.id].error = 'Circular dependency detected'
+            context.node_states[node.id].end_time = datetime.now()
+            await self._persist_node_execution(node, context.node_states[node.id], context)
+            return
+
+        # Add node to current path
+        path.add(node.id)
+
+        # Check if node should be skipped
+        if await self._should_skip_node(node, context):
+            existing_state = context.node_states[node.id]
+            if existing_state.status == NodeStatus.SKIPPED:
+                existing_state.end_time = existing_state.end_time or datetime.now()
+                await self._persist_node_execution(node, existing_state, context)
+            path.discard(node.id)
+            return
+
+        # Execute current node
+        await self._execute_node(node, context, max_retries)
+
+        # If node failed and we should stop on error, return
+        if context.node_states[node.id].status == NodeStatus.FAILED:
+            path.discard(node.id)
+            return
+
+        node_state = context.node_states[node.id]
+        node_type_name = node.type.split('.')[-1] if '.' in node.type else node.type
+
+        # ── Control flow integration ────────────────────────────────
+        # For loop / iterator nodes: run the LoopExecutor over
+        # downstream body nodes for each item, then continue to the
+        # "completed" output edge.
+        if node_type_name in ('loop', 'iterator'):
+            items = node_state.outputs.get('_items') or []
+            if not items:
+                # iterator: items come from inputs
+                items = node_state.inputs.get('items', node_state.inputs.get('array', []))
+                if not isinstance(items, list):
+                    items = [items] if items else []
+            max_iter = int(node.config.get('max_iterations', 100))
+            items = items[:max_iter]
+
+            # Collect downstream "body" nodes (connected via edges)
+            outgoing_edges = edge_map.get(node.id, [])
+            body_nodes = []
+            for edge in outgoing_edges:
+                target = node_map.get(edge.target_node)
+                if target:
+                    body_nodes.append(target)
+
+            if body_nodes and items:
+                loop_exec = LoopExecutor(self)
+                results = await loop_exec.execute_loop(items, body_nodes, context, max_iter)
+                node_state.outputs['results'] = results
+                node_state.outputs['completed'] = True
+            else:
+                node_state.outputs['results'] = []
+                node_state.outputs['completed'] = True
+
+            path.discard(node.id)
+            return  # body nodes already executed by LoopExecutor
+
+        # For parallel nodes: run downstream branches concurrently
+        if node_type_name == 'parallel':
+            outgoing_edges = edge_map.get(node.id, [])
+            branch_nodes = []
+            for edge in outgoing_edges:
+                target = node_map.get(edge.target_node)
+                if target:
+                    branch_nodes.append([target])
+
+            if branch_nodes:
+                par_exec = ParallelExecutor(self)
+                results = await par_exec.execute_parallel(branch_nodes, context)
+                node_state.outputs['results'] = results
+
+            path.discard(node.id)
+            return  # branch nodes already executed by ParallelExecutor
+
+        # ── Standard edge-based continuation ────────────────────────
+        # Get outgoing edges
+        outgoing_edges = edge_map.get(node.id, [])
+
+        # Execute next nodes based on edge conditions
+        for edge in outgoing_edges:
+            target_node = node_map.get(edge.target_node)
+            if not target_node:
+                continue
+
+            # Check edge condition
+            if edge.condition:
+                condition_met = await self._evaluate_condition(edge.condition, context)
+                if not condition_met:
+                    continue
+
+            # Check if all inputs are ready
+            if await self._inputs_ready(target_node, edge_map, context):
+                await self._execute_from_node(target_node, node_map, edge_map, context, max_retries, path)
+
+        # Remove node from path when backtracking (allows diamond revisit)
+        path.discard(node.id)
+
+    async def _execute_node(self, node: NodeDefinition, context: ExecutionContext, max_retries: int = 3):
+        """Execute a single node with retry logic"""
+
+        node_state = context.node_states[node.id]
+        node_state.status = NodeStatus.RUNNING
+        node_state.start_time = datetime.now()
+
+        # Get node instance (pass ap for access to services)
+        node_instance = self.registry.create_instance(node.type, node.id, node.config, ap=self.ap)
+
+        if not node_instance:
+            node_state.status = NodeStatus.FAILED
+            node_state.error = f'Unknown node type: {node.type}'
+            node_state.end_time = datetime.now()
+            self._record_execution_step(node, node_state, context)
+            await self._persist_node_execution(node, node_state, context)
+            return
+
+        # Resolve inputs
+        inputs = await self._resolve_inputs(node, context)
+        node_state.inputs = inputs
+
+        # Validate inputs
+        validation_errors = await node_instance.validate_inputs(inputs)
+        if validation_errors:
+            node_state.status = NodeStatus.FAILED
+            node_state.error = '; '.join(validation_errors)
+            node_state.end_time = datetime.now()
+            self._record_execution_step(node, node_state, context)
+            await self._persist_node_execution(node, node_state, context)
+            return
+
+        # Check if node supports streaming (has execute_stream method and stream config is enabled)
+        use_streaming = hasattr(node_instance, 'execute_stream') and node.config.get('stream', False)
+
+        # Execute with retries
+        for attempt in range(max_retries + 1):
+            try:
+                if use_streaming:
+                    # Streaming execution with aggregation and timeout
+                    aggregated_response = ''
+                    try:
+                        async with asyncio.timeout(300):  # 5 minute timeout for streaming
+                            async for chunk in node_instance.execute_stream(inputs, context):
+                                if chunk:
+                                    aggregated_response += chunk
+                    except asyncio.TimeoutError:
+                        logger.warning(f'Node {node.id} ({node.type}) streaming timed out, falling back to non-streaming')
+                        use_streaming = False
+                        outputs = await node_instance.execute(inputs, context)
+                    else:
+                        # Get response from context if set by execute_stream, otherwise use aggregated
+                        final_response = context.variables.pop('_last_llm_response', aggregated_response)
+                        outputs = {'response': final_response, 'usage': {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}}
+                        logger.info(f'Node {node.id} ({node.type}) streaming completed, response length: {len(final_response)}')
+                else:
+                    outputs = await node_instance.execute(inputs, context)
+                node_state.outputs = outputs
+                node_state.status = NodeStatus.COMPLETED
+                node_state.end_time = datetime.now()
+                break
+            except Exception as e:
+                node_state.retry_count = attempt + 1
+                logger.error(
+                    f'Node {node.id} ({node.type}) execution failed (attempt {attempt + 1}/{max_retries + 1}): {e}',
+                    exc_info=True,
+                    extra={
+                        'node_id': node.id,
+                        'node_type': node.type,
+                        'attempt': attempt + 1,
+                        'max_retries': max_retries,
+                        'execution_id': context.execution_id,
+                    },
+                )
+
+                if attempt < max_retries:
+                    await asyncio.sleep(1)  # Brief delay before retry
+                else:
+                    node_state.status = NodeStatus.FAILED
+                    node_state.error = str(e)
+                    node_state.end_time = datetime.now()
+                    logger.error(
+                        f'Node {node.id} ({node.type}) permanently failed after {max_retries + 1} attempts',
+                        extra={
+                            'node_id': node.id,
+                            'node_type': node.type,
+                            'error': str(e),
+                            'execution_id': context.execution_id,
+                        },
+                    )
+
+        self._record_execution_step(node, node_state, context)
+        await self._persist_node_execution(node, node_state, context)
+
+    async def _resolve_inputs(self, node: NodeDefinition, context: ExecutionContext) -> dict[str, Any]:
+        """Resolve input values for a node from connected nodes and context"""
+        inputs = {}
+
+        # Get inputs from context variables
+        if 'message' in context.variables:
+            inputs['message'] = context.variables['message']
+
+        # Get inputs from message context
+        if context.message_context:
+            inputs['message'] = context.message_context.message_content
+            inputs['message_content'] = context.message_context.message_content
+            inputs['sender_id'] = context.message_context.sender_id
+            inputs['platform'] = context.message_context.platform
+        else:
+            logger.warning(
+                f'[_resolve_inputs] node={node.id} ({node.type}): message_context is None!',
+                extra={
+                    'node_id': node.id,
+                    'node_type': node.type,
+                    'execution_id': context.execution_id,
+                    'variables_keys': list(context.variables.keys()) if context.variables else [],
+                },
+            )
+
+        # Log current inputs state after message_context processing
+        logger.debug(
+            f'[_resolve_inputs] node={node.id} after message_context: {list(inputs.keys())}',
+        )
+
+        # Get inputs from node config that reference other nodes
+        for key, value in node.config.items():
+            if isinstance(value, str) and value.startswith('{{') and value.endswith('}}'):
+                resolved = await self._resolve_expression(value[2:-2], context)
+                inputs[key] = resolved
+            else:
+                inputs[key] = value
+
+        # Get inputs from connected upstream nodes via data edges.
+        # Build a reverse map: for each incoming edge to this node, find the
+        # source node and the specific source/target port.
+        for edge in self._edges:
+            if not self._is_data_edge(edge):
+                continue
+            if edge.target_node != node.id:
+                continue
+            source_state = context.node_states.get(edge.source_node)
+            if not source_state or source_state.status != NodeStatus.COMPLETED:
+                continue
+            target_port = edge.target_port or 'input'
+            source_port = edge.source_port or 'output'
+            # Map the source node's output port value to this node's input port
+            if source_port in source_state.outputs:
+                inputs[target_port] = source_state.outputs[source_port]
+            elif 'output' in source_state.outputs:
+                # Fallback: if exact port not found, try generic 'output'
+                inputs[target_port] = source_state.outputs['output']
+            elif source_state.outputs:
+                # Last resort: use the first available output
+                inputs[target_port] = next(iter(source_state.outputs.values()))
+
+        # Smart input mapping: if a node needs 'message' but received a different
+        # port name (e.g., 'content' from llm_call), copy the value to 'message'.
+        # This handles edge connection mismatches where the sender uses a different
+        # port name than what the receiver expects.
+        if 'message' not in inputs or inputs.get('message') is None:
+            for fallback_key in ('content', 'response', 'input', 'output', 'result', 'text'):
+                if fallback_key in inputs and inputs[fallback_key] is not None:
+                    inputs['message'] = inputs[fallback_key]
+                    logger.debug(
+                        f'[_resolve_inputs] node={node.id}: mapped {fallback_key} -> message',
+                    )
+                    break
+
+        logger.debug(
+            f'[_resolve_inputs] node={node.id} final inputs keys: {list(inputs.keys())}, message={repr(inputs.get("message", "<missing>")[:100] if isinstance(inputs.get("message"), str) else inputs.get("message"))}',
+        )
+        return inputs
+
+    async def _resolve_expression(self, expression: str, context: ExecutionContext) -> Any:
+        """Resolve a variable expression like 'nodes.node1.outputs.text'"""
+        parts = expression.strip().split('.')
+
+        if not parts:
+            return None
+
+        if parts[0] == 'nodes' and len(parts) >= 4:
+            # nodes.node_id.outputs.output_name
+            node_id = parts[1]
+            if parts[2] == 'outputs' and node_id in context.node_states:
+                output_name = '.'.join(parts[3:])
+                return context.node_states[node_id].outputs.get(output_name)
+
+        elif parts[0] == 'variables':
+            # variables.var_name
+            var_name = '.'.join(parts[1:])
+            return context.variables.get(var_name)
+
+        elif parts[0] == 'conversation_variables':
+            # conversation_variables.var_name
+            var_name = '.'.join(parts[1:])
+            return context.conversation_variables.get(var_name)
+
+        elif parts[0] == 'message':
+            # message.content, message.sender_id, etc.
+            if context.message_context:
+                attr = parts[1] if len(parts) > 1 else None
+                if attr == 'content':
+                    return context.message_context.message_content
+                elif attr == 'sender_id':
+                    return context.message_context.sender_id
+                elif attr == 'platform':
+                    return context.message_context.platform
+                elif attr == 'conversation_id':
+                    return context.message_context.conversation_id
+
+        return None
+
+    async def _evaluate_condition(self, condition: str, context: ExecutionContext) -> bool:
+        """Evaluate a condition expression safely.
+
+        Any ``{{ ... }}`` references are resolved against the execution context
+        and bound as **variables** that are passed to :func:`safe_eval_with_vars`.
+        Values are never string-concatenated into the expression, which avoids
+        broken parsing (e.g. values containing quotes) and any injection risk
+        from non-literal value types (lists, dicts, etc.).
+        """
+        variables: dict[str, Any] = {}
+        try:
+            # Resolve variable references in condition into bound variables.
+            if '{{' in condition:
+                pattern = r'\{\{([^}]+)\}\}'
+
+                placeholders: dict[str, str] = {}
+                placeholder_idx = 0
+
+                def replace_with_placeholder(match: re.Match[str]) -> str:
+                    nonlocal placeholder_idx
+                    var_expr = match.group(1)
+                    placeholder = f'__ph{placeholder_idx}__'
+                    placeholders[placeholder] = var_expr
+                    placeholder_idx += 1
+                    return placeholder
+
+                condition = re.sub(pattern, replace_with_placeholder, condition)
+
+                # Resolve each placeholder and bind it as a variable, so the
+                # actual value (of any type) is passed through unchanged.
+                for placeholder, var_expr in placeholders.items():
+                    variables[placeholder] = await self._resolve_expression(var_expr, context)
+
+            # Safe expression evaluation with bound variables (AST whitelist).
+            result = safe_eval_with_vars(condition, variables)
+            return bool(result)
+
+        except Exception as e:
+            logger.warning(f'Condition evaluation failed: {condition} - {e}')
+            return False
+
+    async def _should_skip_node(self, node: NodeDefinition, context: ExecutionContext) -> bool:
+        """Check if a node should be skipped"""
+        state = context.node_states.get(node.id)
+        if state and state.status in (NodeStatus.COMPLETED, NodeStatus.RUNNING, NodeStatus.SKIPPED):
+            return True
+        return False
+
+    async def _inputs_ready(
+        self, node: NodeDefinition, edge_map: dict[str, list[EdgeDefinition]], context: ExecutionContext
+    ) -> bool:
+        """Check if all control predecessors and data providers are ready."""
+        incoming_nodes = self._incoming_dependency_nodes(node.id, edge_map)
+
+        # Check if all incoming nodes have completed
+        for source_id in incoming_nodes:
+            state = context.node_states.get(source_id)
+            if not state or state.status not in (NodeStatus.COMPLETED, NodeStatus.SKIPPED):
+                return False
+
+        return True
+
+    def _find_start_nodes(self, nodes: list[NodeDefinition], edges: list[EdgeDefinition]) -> list[NodeDefinition]:
+        """Find nodes that have no incoming edges (start nodes)"""
+        target_nodes = {edge.target_node for edge in edges if self._is_control_edge(edge) or self._is_data_edge(edge)}
+        start_nodes = [node for node in nodes if node.id not in target_nodes]
+
+        # Also check for trigger nodes
+        trigger_types = {'message_trigger', 'cron_trigger', 'webhook_trigger', 'event_trigger'}
+        for node in nodes:
+            if node.type in trigger_types and node not in start_nodes:
+                start_nodes.insert(0, node)
+
+        return start_nodes
+
+    def _build_edge_map(self, edges: list[EdgeDefinition]) -> dict[str, list[EdgeDefinition]]:
+        """Build a map of source node ID to outgoing control edges."""
+        edge_map: dict[str, list[EdgeDefinition]] = {}
+        for edge in edges:
+            if not self._is_control_edge(edge):
+                continue
+            if edge.source_node not in edge_map:
+                edge_map[edge.source_node] = []
+            edge_map[edge.source_node].append(edge)
+        return edge_map
+
+    def _edge_type(self, edge: EdgeDefinition) -> str:
+        edge_type = (getattr(edge, 'edge_type', None) or 'legacy').strip().lower()
+        if edge_type not in {'control', 'data', 'legacy'}:
+            return 'legacy'
+        return edge_type
+
+    def _is_control_edge(self, edge: EdgeDefinition) -> bool:
+        return self._edge_type(edge) in {'control', 'legacy'}
+
+    def _is_data_edge(self, edge: EdgeDefinition) -> bool:
+        return self._edge_type(edge) in {'data', 'legacy'}
+
+    def _incoming_dependency_nodes(
+        self, node_id: str, edge_map: dict[str, list[EdgeDefinition]]
+    ) -> set[str]:
+        incoming_nodes: set[str] = set()
+        for source_id, edges in edge_map.items():
+            for edge in edges:
+                if edge.target_node == node_id:
+                    incoming_nodes.add(source_id)
+
+        for edge in self._edges:
+            if self._is_data_edge(edge) and edge.target_node == node_id:
+                incoming_nodes.add(edge.source_node)
+
+        return incoming_nodes
+
+    def _record_execution_step(self, node: NodeDefinition, node_state: NodeState, context: ExecutionContext):
+        """Record an execution step in the history"""
+        duration_ms = 0
+        if node_state.start_time and node_state.end_time:
+            duration_ms = int((node_state.end_time - node_state.start_time).total_seconds() * 1000)
+
+        step = ExecutionStep(
+            step_id=f"step_{uuid.uuid4().hex[:8]}",
+            timestamp=datetime.now(),
+            node_id=node.id,
+            node_type=node.type,
+            status=node_state.status,
+            duration_ms=duration_ms,
+            error=node_state.error,
+            inputs=node_state.inputs,
+            outputs=node_state.outputs,
+        )
+        context.history.append(step)
+
+    async def _persist_node_execution(
+        self,
+        node: NodeDefinition,
+        node_state: NodeState,
+        context: ExecutionContext,
+    ):
+        """Persist node execution state for execution detail and logs."""
+        if not self.ap:
+            return
+
+        values = {
+            'execution_uuid': context.execution_id,
+            'node_id': node.id,
+            'node_type': node.type,
+            'status': node_state.status.value,
+            'inputs': node_state.inputs,
+            'outputs': node_state.outputs,
+            'start_time': node_state.start_time,
+            'end_time': node_state.end_time,
+            'error': node_state.error,
+            'retry_count': node_state.retry_count,
+        }
+
+        existing_query = sqlalchemy.select(persistence_workflow.WorkflowNodeExecution).where(
+            persistence_workflow.WorkflowNodeExecution.execution_uuid == context.execution_id,
+            persistence_workflow.WorkflowNodeExecution.node_id == node.id,
+        )
+        existing_result = await self.ap.persistence_mgr.execute_async(existing_query)
+        existing = existing_result.first()
+
+        if existing is None:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.insert(persistence_workflow.WorkflowNodeExecution).values(**values)
+            )
+        else:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_workflow.WorkflowNodeExecution)
+                .where(persistence_workflow.WorkflowNodeExecution.id == existing.id)
+                .values(**values)
+            )
+
+
+class ParallelExecutor:
+    """Execute multiple branches in parallel"""
+
+    def __init__(self, executor: WorkflowExecutor):
+        self.executor = executor
+
+    async def execute_parallel(
+        self, branches: list[list[NodeDefinition]], context: ExecutionContext
+    ) -> list[dict[str, Any]]:
+        """
+        Execute multiple branches in parallel.
+
+        Args:
+            branches: List of node sequences to execute in parallel
+            context: Execution context
+
+        Returns:
+            List of results from each branch
+        """
+        tasks = []
+        for branch in branches:
+            task = self._execute_branch(branch, context)
+            tasks.append(task)
+
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        processed_results = []
+        for index, result in enumerate(results):
+            if isinstance(result, Exception):
+                logger.error(
+                    f'Parallel branch {index} failed: {result}',
+                    exc_info=result,
+                    extra={'branch_index': index, 'execution_id': context.execution_id},
+                )
+                processed_results.append({'error': str(result)})
+            else:
+                processed_results.append(result)
+
+        return processed_results
+
+    async def _execute_branch(self, nodes: list[NodeDefinition], context: ExecutionContext) -> dict[str, Any]:
+        """Execute a single branch"""
+        # Create a copy of context for this branch
+        branch_outputs = {}
+
+        for node in nodes:
+            await self.executor._execute_node(node, context, max_retries=3)
+            state = context.node_states.get(node.id)
+            if state and state.status == NodeStatus.COMPLETED:
+                branch_outputs[node.id] = state.outputs
+            elif state and state.status == NodeStatus.FAILED:
+                branch_outputs['error'] = state.error
+                break
+
+        return branch_outputs
+
+
+class LoopExecutor:
+    """Execute loop iterations"""
+
+    def __init__(self, executor: WorkflowExecutor):
+        self.executor = executor
+
+    async def execute_loop(
+        self, items: list[Any], loop_body: list[NodeDefinition], context: ExecutionContext, max_iterations: int = 100
+    ) -> list[dict[str, Any]]:
+        """
+        Execute a loop over items.
+
+        Args:
+            items: Items to iterate over
+            loop_body: Nodes to execute for each item
+            context: Execution context
+            max_iterations: Maximum number of iterations
+
+        Returns:
+            List of results from each iteration
+        """
+        results = []
+
+        for i, item in enumerate(items[:max_iterations]):
+            # Set loop variables
+            context.variables['loop_item'] = item
+            context.variables['loop_index'] = i
+            context.variables['loop_is_first'] = i == 0
+            context.variables['loop_is_last'] = i == len(items) - 1
+
+            iteration_result = {}
+
+            for node in loop_body:
+                # Reset node state for this iteration
+                context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
+
+                await self.executor._execute_node(node, context, max_retries=3)
+
+                state = context.node_states.get(node.id)
+                if state:
+                    iteration_result[node.id] = state.outputs
+
+                    # Check for break condition
+                    if state.outputs.get('break', False):
+                        results.append(iteration_result)
+                        return results
+
+            results.append(iteration_result)
+
+        # Clean up loop variables
+        context.variables.pop('loop_item', None)
+        context.variables.pop('loop_index', None)
+        context.variables.pop('loop_is_first', None)
+        context.variables.pop('loop_is_last', None)
+
+        return results

src/langbot/pkg/workflow/metadata.py

@@ -0,0 +1,284 @@
+"""Workflow node metadata loading and validation.
+
+This module makes YAML files under ``templates/metadata/nodes`` the backend
+source of truth for workflow node metadata. Python node classes still provide
+execution logic, but UI-facing metadata is loaded from YAML.
+"""
+
+from __future__ import annotations
+
+import copy
+import logging
+from importlib import resources
+from pathlib import Path
+from typing import Any, Iterable, Optional
+
+import yaml
+
+logger = logging.getLogger(__name__)
+
+
+class MetadataLoadError(Exception):
+    """Raised when a workflow node metadata file cannot be loaded."""
+
+
+class MetadataValidationError(Exception):
+    """Raised when workflow node metadata does not match the expected shape."""
+
+
+class NodeMetadataValidator:
+    """Validate workflow node metadata loaded from YAML files.
+
+    The validator is intentionally strict about the structural fields that the
+    editor needs, but tolerant of legacy YAML details such as missing top-level
+    ``label`` or additional frontend field types.
+    """
+
+    REQUIRED_FIELDS = ('name', 'category', 'inputs', 'outputs', 'config')
+    VALID_CATEGORIES = {'trigger', 'process', 'control', 'action', 'integration', 'misc'}
+    VALID_PORT_TYPES = {'any', 'string', 'number', 'integer', 'boolean', 'object', 'array', 'datetime', 'null'}
+    VALID_CONFIG_TYPES = {
+        'string',
+        'integer',
+        'number',
+        'float',
+        'boolean',
+        'select',
+        'json',
+        'textarea',
+        'text',
+        'secret',
+        'array[string]',
+        'file',
+        'array[file]',
+        'llm-model-selector',
+        'embedding-model-selector',
+        'rerank-model-selector',
+        'pipeline-selector',
+        'knowledge-base-selector',
+        'knowledge-base-multi-selector',
+        'bot-selector',
+        'tools-selector',
+        'model-fallback-selector',
+        'prompt-editor',
+        'plugin-selector',
+        'webhook-url',
+        'embed-code',
+        'workflow-selector',
+    }
+
+    def validate(self, metadata: dict[str, Any]) -> list[str]:
+        """Return validation errors. An empty list means the metadata is valid."""
+        errors: list[str] = []
+
+        if not isinstance(metadata, dict):
+            return ['metadata root must be a mapping']
+
+        for field in self.REQUIRED_FIELDS:
+            if field not in metadata:
+                errors.append(f'missing required field: {field}')
+
+        if errors:
+            return errors
+
+        name = metadata.get('name')
+        if not isinstance(name, str) or not name.strip():
+            errors.append('field "name" must be a non-empty string')
+
+        category = metadata.get('category')
+        if category not in self.VALID_CATEGORIES:
+            errors.append(f'invalid category: {category}')
+
+        errors.extend(self._validate_ports(metadata.get('inputs'), 'inputs'))
+        errors.extend(self._validate_ports(metadata.get('outputs'), 'outputs'))
+        errors.extend(self._validate_config(metadata.get('config')))
+
+        return errors
+
+    def validate_or_raise(self, metadata: dict[str, Any]) -> dict[str, Any]:
+        """Validate metadata and raise ``MetadataValidationError`` on failure."""
+        errors = self.validate(metadata)
+        if errors:
+            node_name = metadata.get('name', 'unknown') if isinstance(metadata, dict) else 'unknown'
+            raise MetadataValidationError(f'invalid metadata for {node_name}: {errors}')
+        return metadata
+
+    def _validate_ports(self, ports: Any, field_name: str) -> list[str]:
+        errors: list[str] = []
+        if not isinstance(ports, list):
+            return [f'{field_name} must be a list']
+
+        seen_names: set[str] = set()
+        for index, port in enumerate(ports):
+            path = f'{field_name}[{index}]'
+            if not isinstance(port, dict):
+                errors.append(f'{path} must be a mapping')
+                continue
+
+            name = port.get('name')
+            if not isinstance(name, str) or not name:
+                errors.append(f'{path}.name must be a non-empty string')
+                continue
+
+            if name in seen_names:
+                errors.append(f'{path}.name duplicates "{name}"')
+            seen_names.add(name)
+
+            port_type = port.get('type', 'any')
+            if port_type not in self.VALID_PORT_TYPES:
+                errors.append(f'{path}.type has unsupported value "{port_type}"')
+
+        return errors
+
+    def _validate_config(self, config: Any) -> list[str]:
+        errors: list[str] = []
+        if not isinstance(config, list):
+            return ['config must be a list']
+
+        seen_names: set[str] = set()
+        for index, item in enumerate(config):
+            path = f'config[{index}]'
+            if not isinstance(item, dict):
+                errors.append(f'{path} must be a mapping')
+                continue
+
+            name = item.get('name')
+            if not isinstance(name, str) or not name:
+                errors.append(f'{path}.name must be a non-empty string')
+                continue
+
+            if name in seen_names:
+                errors.append(f'{path}.name duplicates "{name}"')
+            seen_names.add(name)
+
+            item_type = item.get('type', 'string')
+            if item_type not in self.VALID_CONFIG_TYPES:
+                errors.append(f'{path}.type has unsupported value "{item_type}"')
+
+            min_value = item.get('min_value')
+            max_value = item.get('max_value')
+            if isinstance(min_value, (int, float)) and isinstance(max_value, (int, float)) and min_value > max_value:
+                errors.append(f'{path}.min_value must be <= max_value')
+
+        return errors
+
+
+class NodeMetadataLoader:
+    """Load and cache workflow node metadata from YAML files."""
+
+    def __init__(self, validator: Optional[NodeMetadataValidator] = None) -> None:
+        self._validator = validator or NodeMetadataValidator()
+        self._metadata: dict[str, dict[str, Any]] = {}
+        self._sources: dict[str, str] = {}
+        self._load_errors: list[dict[str, str]] = []
+
+    async def load_core_metadata(self, resource_dir: str = 'metadata/nodes') -> int:
+        """Load all core node metadata from the ``langbot.templates`` package."""
+        return await self.load_package_directory('langbot.templates', resource_dir, source='core')
+
+    async def load_package_directory(self, package: str, resource_dir: str, source: str = 'core') -> int:
+        """Load YAML files from a package resource directory."""
+        try:
+            root = resources.files(package).joinpath(resource_dir)
+            yaml_files = sorted(
+                (item for item in root.iterdir() if item.is_file() and item.name.endswith(('.yaml', '.yml'))),
+                key=lambda item: item.name,
+            )
+        except Exception as exc:
+            raise MetadataLoadError(f'failed to scan package directory {package}:{resource_dir}: {exc}') from exc
+
+        return self._load_files(yaml_files, source=source)
+
+    async def load_directory(self, directory: str | Path, source: str) -> int:
+        """Load YAML files from an external filesystem directory, e.g. a plugin."""
+        directory_path = Path(directory)
+        if not directory_path.exists():
+            logger.warning('Workflow metadata directory does not exist: %s', directory_path)
+            return 0
+        if not directory_path.is_dir():
+            raise MetadataLoadError(f'workflow metadata path is not a directory: {directory_path}')
+
+        yaml_files = sorted(directory_path.glob('*.yml')) + sorted(directory_path.glob('*.yaml'))
+        return self._load_files(yaml_files, source=source)
+
+    def get_metadata(self, node_type: str) -> Optional[dict[str, Any]]:
+        """Return metadata by full type or short node name."""
+        if node_type in self._metadata:
+            return copy.deepcopy(self._metadata[node_type])
+
+        short_name = node_type.split('.')[-1]
+        for registered_type, metadata in self._metadata.items():
+            if registered_type.split('.')[-1] == short_name or metadata.get('name') == short_name:
+                return copy.deepcopy(metadata)
+
+        return None
+
+    def get_all_metadata(self) -> dict[str, dict[str, Any]]:
+        """Return a deep copy of all loaded metadata keyed by canonical node type."""
+        return copy.deepcopy(self._metadata)
+
+    def get_load_errors(self) -> list[dict[str, str]]:
+        """Return metadata files that failed to load or validate."""
+        return copy.deepcopy(self._load_errors)
+
+    def clear(self) -> None:
+        """Clear all cached metadata and errors."""
+        self._metadata.clear()
+        self._sources.clear()
+        self._load_errors.clear()
+
+    def _load_files(self, yaml_files: Iterable[Any], source: str) -> int:
+        count = 0
+        for yaml_file in yaml_files:
+            file_name = getattr(yaml_file, 'name', str(yaml_file))
+            try:
+                metadata = self._load_yaml(yaml_file)
+                self._validator.validate_or_raise(metadata)
+                node_type = build_node_type(metadata)
+
+                if node_type in self._metadata:
+                    existing_source = self._sources.get(node_type, 'unknown')
+                    if existing_source == 'core' and source != 'core':
+                        raise MetadataLoadError(
+                            f'plugin source "{source}" attempted to override core node "{node_type}"'
+                        )
+                    logger.warning(
+                        'Workflow node metadata %s from %s overrides previous source %s',
+                        node_type,
+                        source,
+                        existing_source,
+                    )
+
+                cached_metadata = copy.deepcopy(metadata)
+                cached_metadata['_source'] = source
+                cached_metadata['_file'] = file_name
+                self._metadata[node_type] = cached_metadata
+                self._sources[node_type] = source
+                count += 1
+            except Exception as exc:
+                self._load_errors.append({'file': file_name, 'source': source, 'error': str(exc)})
+                logger.error('Failed to load workflow node metadata %s: %s', file_name, exc)
+
+        return count
+
+    def _load_yaml(self, yaml_file: Any) -> dict[str, Any]:
+        try:
+            if hasattr(yaml_file, 'open'):
+                with yaml_file.open('r', encoding='utf-8') as file:
+                    data = yaml.load(file, Loader=yaml.FullLoader)
+            else:
+                with open(yaml_file, 'r', encoding='utf-8') as file:
+                    data = yaml.load(file, Loader=yaml.FullLoader)
+        except Exception as exc:
+            raise MetadataLoadError(f'failed to parse YAML: {exc}') from exc
+
+        if not isinstance(data, dict):
+            raise MetadataLoadError('YAML root must be a mapping')
+        return data
+
+
+def build_node_type(metadata: dict[str, Any]) -> str:
+    """Build canonical ``category.name`` node type from metadata."""
+    category = metadata.get('category') or 'misc'
+    name = metadata.get('name') or ''
+    return f'{category}.{name}'

src/langbot/pkg/workflow/monitor.py

@@ -0,0 +1,61 @@
+"""
+Monitoring helper for recording events during workflow execution.
+This module provides convenient methods to record monitoring data
+without cluttering the main workflow code.
+
+NOTE: All frontend panel logging functionality has been removed.
+A new solution will be implemented separately.
+"""
+
+from __future__ import annotations
+
+import typing
+import time
+
+if typing.TYPE_CHECKING:
+    from ..core import app
+    from langbot_plugin.api.entities.builtin.workflow.query import WorkflowQuery
+
+
+class WorkflowMonitoringHelper:
+    """Helper class for workflow monitoring operations"""
+
+    # All frontend panel logging methods have been removed.
+    # A new solution will be implemented separately.
+    pass
+
+
+class LLMCallMonitor:
+    """Context manager for monitoring LLM calls in workflow"""
+
+    def __init__(
+        self,
+        ap: app.Application,
+        query: WorkflowQuery,
+        bot_id: str,
+        bot_name: str,
+        workflow_id: str,
+        workflow_name: str,
+        node_name: str,
+        model_name: str,
+    ):
+        self.ap = ap
+        self.query = query
+        self.bot_id = bot_id
+        self.bot_name = bot_name
+        self.workflow_id = workflow_id
+        self.workflow_name = workflow_name
+        self.node_name = node_name
+        self.model_name = model_name
+        self.start_time = None
+        self.input_tokens = 0
+        self.output_tokens = 0
+
+    async def __aenter__(self):
+        self.start_time = time.time()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        # LLM call monitoring has been removed.
+        # A new solution will be implemented separately.
+        return False