new node

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

@@ -323,18 +323,16 @@ class WorkflowsRouterGroup(group.RouterGroup):
                 "model_uuid": "uuid-of-model",
                 "system_prompt": "optional system prompt",
                 "user_prompt": "test message",
-                "enable_tools": false,
-                "tools": [],
                 "temperature": 0.7,
                 "max_tokens": 100
             }
             
             Response includes timing for each step:
             - model_fetch: Time to get model from model_mgr
-            - tool_fetch: Time to load tools (if enabled)
             - prompt_build: Time to build messages
             - llm_call: Time for actual LLM invocation
             - total: Total time
+            - usage: Token usage information
             """
             import time
             
@@ -348,8 +346,6 @@ class WorkflowsRouterGroup(group.RouterGroup):
             
             user_prompt = json_data.get('user_prompt', 'test')
             system_prompt = json_data.get('system_prompt', '')
-            enable_tools = json_data.get('enable_tools', False)
-            tools_config = json_data.get('tools', [])
             temperature = json_data.get('temperature')
             max_tokens = json_data.get('max_tokens', 0)
             
@@ -372,24 +368,7 @@ class WorkflowsRouterGroup(group.RouterGroup):
                     'timings': timings,
                 })
             
-            # Step 2: Tool fetch (if enabled)
+            # Step 2: Build messages
-            timings['tool_fetch_ms'] = 0
-            timings['tools_loaded'] = 0
-            if enable_tools and tools_config:
-                t_start = time.perf_counter()
-                try:
-                    import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
-                    all_tools = await self.ap.tool_mgr.get_tools()
-                    tool_names = tools_config if isinstance(tools_config, list) else []
-                    funcs = [t for t in all_tools if t.name in tool_names]
-                    timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
-                    timings['tools_loaded'] = len(funcs)
-                    timings['tool_names'] = [t.name for t in funcs]
-                except Exception as e:
-                    timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
-                    errors.append(f'Tool fetch failed: {str(e)}')
-            
-            # Step 3: Build messages
             t_start = time.perf_counter()
             import langbot_plugin.api.entities.builtin.provider.message as provider_message
             messages = []
@@ -398,21 +377,21 @@ class WorkflowsRouterGroup(group.RouterGroup):
             messages.append(provider_message.Message(role='user', content=user_prompt))
             timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
             
-            # Step 4: Build extra args
+            # 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 5: LLM call
+            # 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=funcs if enable_tools else None,
+                    funcs=None,
                     extra_args=extra_args,
                 )
                 timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
@@ -451,7 +430,6 @@ class WorkflowsRouterGroup(group.RouterGroup):
             # Calculate total
             timings['total_ms'] = round(sum([
                 timings.get('model_fetch_ms', 0),
-                timings.get('tool_fetch_ms', 0),
                 timings.get('prompt_build_ms', 0),
                 timings.get('llm_call_ms', 0),
             ]), 2)
@@ -460,7 +438,6 @@ class WorkflowsRouterGroup(group.RouterGroup):
             if timings['total_ms'] > 0:
                 timings['breakdown'] = {
                     'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
-                    'tool_fetch_pct': round(timings.get('tool_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),
                 }

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

@@ -117,7 +117,7 @@ class WorkflowService:
             'uuid': workflow_uuid,
             'name': workflow_data.get('name', 'New Workflow'),
             'description': workflow_data.get('description', ''),
-            'emoji': workflow_data.get('emoji', '🔄'),
+            'emoji': workflow_data.get('emoji', '💼'),
             'version': 1,
             'is_enabled': workflow_data.get('is_enabled', True),
             'definition': workflow_data.get(

src/langbot/pkg/workflow/entities.py

@@ -120,7 +120,7 @@ class WorkflowDefinition(pydantic.BaseModel):
     uuid: str
     name: str
     description: str = ''
-    emoji: str = '🔄'
+    emoji: str = '💼'
     version: int = 1
 
     # Workflow graph

src/langbot/pkg/workflow/nodes/__init__.py

@@ -30,6 +30,7 @@ from . import variable_aggregator
 from . import send_message
 from . import reply_message
 from . import call_pipeline
+from . import call_workflow
 from . import store_data
 from . import set_variable
 from . import opening_statement
@@ -74,6 +75,7 @@ __all__ = [
     'send_message',
     'reply_message',
     'call_pipeline',
+    'call_workflow',
     'store_data',
     'set_variable',
     'opening_statement',

src/langbot/pkg/workflow/nodes/call_workflow.py

@@ -0,0 +1,85 @@
+"""Call Workflow Node - invoke an existing workflow
+
+Node metadata is loaded from: ../../templates/metadata/nodes/call_workflow.yaml
+"""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
+
+from ..node import WorkflowNode, workflow_node
+
+
+@workflow_node('call_workflow')
+class CallWorkflowNode(WorkflowNode):
+    """Call workflow node - invoke an existing workflow"""
+
+    category = 'action'
+
+    async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
+        if not self.ap:
+            raise RuntimeError('Application instance not available — cannot call workflow')
+
+        # Get workflow reference from config
+        workflow_ref = str(self.get_config('workflow_uuid', '') or '').strip()
+        if not workflow_ref:
+            raise ValueError('No workflow configured for call workflow node')
+
+        # Get workflow definition from service
+        workflow_data = await self.ap.workflow_service.get_workflow(workflow_ref)
+        if workflow_data is None:
+            raise ValueError(f'Workflow not found: {workflow_ref}')
+
+        workflow_uuid = str(workflow_data.get('uuid', '') or '')
+        if not workflow_uuid:
+            raise ValueError(f'Workflow UUID missing for: {workflow_ref}')
+
+        # Build variables to pass to the called workflow
+        variables = dict(inputs.get('variables', {}) or {})
+
+        # Inherit current workflow variables if configured
+        if self.get_config('inherit_variables', True):
+            for key, value in (context.variables or {}).items():
+                if key not in variables:
+                    variables[key] = value
+
+        # Add context markers for debugging
+        variables['_called_from_workflow'] = True
+        variables['_parent_workflow_id'] = context.workflow_id
+        variables['_parent_execution_id'] = context.execution_id
+
+        # Execute the workflow
+        execution_id = await self.ap.workflow_service.execute_workflow(
+            workflow_uuid=workflow_uuid,
+            trigger_type='workflow_call',
+            trigger_data={
+                'variables': variables,
+                'parent_execution_id': context.execution_id,
+            },
+            session_id=context.session_id,
+            user_id=context.user_id,
+            bot_id=context.bot_id,
+        )
+
+        # Get execution result
+        execution = await self.ap.workflow_service.get_execution(execution_id)
+        if execution is None:
+            raise ValueError(f'Execution result not found: {execution_id}')
+
+        # Build result
+        result = {
+            'workflow_uuid': workflow_uuid,
+            'workflow_name': workflow_data.get('name', ''),
+            'execution_id': execution_id,
+            'status': execution.get('status', 'unknown'),
+            'variables': execution.get('variables', {}),
+            'error': execution.get('error'),
+        }
+
+        return {
+            'result': result,
+            'status': execution.get('status', 'unknown'),
+            'error': execution.get('error'),
+        }

src/langbot/pkg/workflow/nodes/http_request.py

@@ -7,7 +7,6 @@ from __future__ import annotations
 
 import ipaddress
 import logging
-import re
 from typing import Any
 from urllib.parse import urlparse
 

src/langbot/pkg/workflow/nodes/llm_call.py

@@ -1,4 +1,11 @@
-"""LLM Call Node - invoke large language model."""
+"""LLM Call Node - invoke large language model with Agent capabilities.
+
+Supports:
+- Primary model with fallback models
+- Knowledge base retrieval with reranking
+- Max round context control
+- Streaming output
+"""
 
 from __future__ import annotations
 
@@ -8,7 +15,7 @@ import re
 from typing import Any, AsyncGenerator
 
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
-import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
+import langbot_plugin.api.entities.builtin.rag.context as rag_context
 
 from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
@@ -133,29 +140,20 @@ class LLMCallNode(WorkflowNode):
 
         return text, False, ''
 
-    def _parse_tools_config(self, tools_config: Any) -> list[dict]:
+    # RAG combined prompt template (same as localagent.py)
-        """Parse tools configuration from YAML config format."""
+    RAG_COMBINED_PROMPT_TEMPLATE = """
-        if not tools_config:
+The following are relevant context entries retrieved from the knowledge base.
-            return []
+Please use them to answer the user's message.
+Respond in the same language as the user's input.
 
-        # If it's already a list, return as-is
+<context>
-        if isinstance(tools_config, list):
+{rag_context}
-            return tools_config
+</context>
 
-        # If it's a string, try to parse as JSON
+<user_message>
-        if isinstance(tools_config, str):
+{user_message}
-            tools_config = tools_config.strip()
+</user_message>
-            if not tools_config:
+"""
-                return []
-            try:
-                parsed = json.loads(tools_config)
-                if isinstance(parsed, list):
-                    return parsed
-            except json.JSONDecodeError:
-                logger.warning(f'Failed to parse tools config as JSON: {tools_config}')
-                return []
-
-        return []
 
     def _build_system_prompt_with_format(self, base_prompt: str, output_format: str, json_schema: str) -> str:
         """Build system prompt with output format instructions."""
@@ -170,6 +168,220 @@ class LLMCallNode(WorkflowNode):
 
         return prompt
 
+    def _build_messages_from_prompt_array(
+        self,
+        prompt_array: list[dict],
+        inputs: dict[str, Any],
+        context: ExecutionContext,
+        output_format: str,
+        json_schema: str,
+    ) -> list[provider_message.Message]:
+        """Build messages list from prompt array (same format as pipeline).
+        
+        Each item in prompt_array is {role: str, content: str}.
+        Resolves template variables in content.
+        """
+        messages: list[provider_message.Message] = []
+        
+        for item in prompt_array:
+            role = item.get('role', 'user')
+            content = item.get('content', '')
+            
+            # Resolve template variables in content
+            resolved_content = self._resolve_template(content, inputs, context)
+            
+            # Apply format instructions to system prompt
+            if role == 'system':
+                resolved_content = self._build_system_prompt_with_format(
+                    resolved_content, output_format, json_schema
+                )
+            
+            messages.append(provider_message.Message(role=role, content=resolved_content))
+        
+        return messages
+
+    async def _get_model_candidates(self, model_uuid: str, fallback_models: list) -> list:
+        """Build ordered list of models to try: primary model + fallback models."""
+        candidates = []
+
+        # Primary model
+        if model_uuid:
+            try:
+                primary = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
+                candidates.append(primary)
+            except ValueError:
+                logger.warning(f'[LLM:{self.node_id}] Primary model {model_uuid} not found')
+
+        # Fallback models
+        for fb_uuid in fallback_models:
+            try:
+                fb_model = await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
+                candidates.append(fb_model)
+            except ValueError:
+                logger.warning(f'[LLM:{self.node_id}] Fallback model {fb_uuid} not found, skipping')
+
+        return candidates
+
+    async def _invoke_with_fallback(
+        self,
+        candidates: list,
+        messages: list,
+        funcs: list | None,
+        extra_args: dict,
+    ) -> tuple[Any, Any]:
+        """Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
+        last_error = None
+        for model in candidates:
+            try:
+                msg = await model.provider.invoke_llm(
+                    query=None,
+                    model=model,
+                    messages=messages,
+                    funcs=funcs if model.model_entity.abilities.__contains__('func_call') else [],
+                    extra_args=extra_args,
+                )
+                return msg, model
+            except Exception as e:
+                last_error = e
+                logger.warning(f'[LLM:{self.node_id}] Model {model.model_entity.name} failed: {e}, trying next...')
+        raise last_error or RuntimeError('No model candidates available')
+
+    async def _retrieve_knowledge(
+        self,
+        user_message_text: str,
+        knowledge_bases: list[str],
+        rerank_model_uuid: str,
+        rerank_top_k: int,
+    ) -> str:
+        """Retrieve from knowledge bases and optionally rerank results.
+        
+        Returns the enhanced user message text with RAG context, or original text if no results.
+        """
+        if not knowledge_bases or not user_message_text:
+            return user_message_text
+
+        all_results: list[rag_context.RetrievalResultEntry] = []
+
+        # Retrieve from each knowledge base
+        for kb_uuid in knowledge_bases:
+            try:
+                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+                if not kb:
+                    logger.warning(f'[LLM:{self.node_id}] Knowledge base {kb_uuid} not found, skipping')
+                    continue
+
+                result = await kb.retrieve(user_message_text, settings={})
+                if result:
+                    all_results.extend(result)
+            except Exception as e:
+                logger.warning(f'[LLM:{self.node_id}] Failed to retrieve from KB {kb_uuid}: {e}')
+
+        # Rerank step: re-score results using a rerank model if configured
+        if all_results and rerank_model_uuid:
+            try:
+                rerank_model = await self.ap.model_mgr.get_rerank_model_by_uuid(rerank_model_uuid)
+                
+                doc_texts = []
+                for entry in all_results:
+                    text = ' '.join(c.text for c in entry.content if c.type == 'text' and c.text)
+                    doc_texts.append(text)
+
+                doc_texts_capped = doc_texts[:64]  # Cap for reranker input
+                scores = await rerank_model.provider.invoke_rerank(
+                    model=rerank_model,
+                    query=user_message_text,
+                    documents=doc_texts_capped,
+                )
+
+                scored = sorted(scores, key=lambda x: x.get('relevance_score', 0), reverse=True)
+                top_indices = [s['index'] for s in scored[:rerank_top_k] if s['index'] < len(all_results)]
+                all_results = [all_results[i] for i in top_indices]
+
+                logger.info(
+                    f'[LLM:{self.node_id}] Rerank complete: {len(doc_texts)} docs -> top {len(all_results)} kept (top_k={rerank_top_k})'
+                )
+            except ValueError:
+                logger.warning(f'[LLM:{self.node_id}] Rerank model {rerank_model_uuid} not found, skipping rerank')
+            except Exception as e:
+                logger.warning(f'[LLM:{self.node_id}] Rerank failed, using original order: {e}')
+
+        # Build RAG context text
+        if all_results:
+            texts = []
+            idx = 1
+            for entry in all_results:
+                for content in entry.content:
+                    if content.type == 'text' and content.text is not None:
+                        texts.append(f'[{idx}] {content.text}')
+                        idx += 1
+            rag_context_text = '\n\n'.join(texts)
+            return self.RAG_COMBINED_PROMPT_TEMPLATE.format(
+                rag_context=rag_context_text,
+                user_message=user_message_text,
+            )
+
+        return user_message_text
+
+    def _build_messages_with_history(
+        self,
+        system_prompt: str,
+        user_message_text: str,
+        context: ExecutionContext,
+        max_round: int,
+    ) -> list[provider_message.Message]:
+        """Build messages list with conversation history up to max_round."""
+        messages: list[provider_message.Message] = []
+        
+        # Add system prompt
+        if system_prompt:
+            messages.append(provider_message.Message(role='system', content=system_prompt))
+
+        # Get conversation history from context
+        conversation_history = context.variables.get('_conversation_history', [])
+        
+        # Apply max_round limit (each round = 1 user + 1 assistant message)
+        if max_round > 0 and conversation_history:
+            # Keep only the last max_round * 2 messages (user + assistant pairs)
+            max_messages = max_round * 2
+            if len(conversation_history) > max_messages:
+                conversation_history = conversation_history[-max_messages:]
+
+        # Add conversation history
+        for msg in conversation_history:
+            if isinstance(msg, dict):
+                role = msg.get('role', 'user')
+                content = msg.get('content', '')
+                messages.append(provider_message.Message(role=role, content=content))
+            elif hasattr(msg, 'role') and hasattr(msg, 'content'):
+                messages.append(provider_message.Message(role=msg.role, content=msg.content))
+
+        # Add current user message
+        messages.append(provider_message.Message(role='user', content=user_message_text))
+
+        return messages
+
+    def _save_to_conversation_history(
+        self,
+        context: ExecutionContext,
+        user_message_text: str,
+        response_text: str,
+        max_round: int,
+    ) -> None:
+        """Save the exchange to conversation history."""
+        if max_round <= 0:
+            return
+
+        history = context.variables.get('_conversation_history', [])
+        history.append({'role': 'user', 'content': user_message_text})
+        history.append({'role': 'assistant', 'content': response_text})
+
+        # Enforce max_round limit
+        max_messages = max_round * 2
+        if len(history) > max_messages:
+            history = history[-max_messages:]
+
+        context.variables['_conversation_history'] = history
+
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         model_uuid = self.get_config('model', '')
         if not model_uuid:
@@ -187,28 +399,90 @@ class LLMCallNode(WorkflowNode):
         output_format = self.get_config('output_format', 'text')
         json_schema = self.get_config('json_schema', '')
 
-        # Get tools config
+        # Agent config: fallback models, knowledge bases, rerank, max_round
-        enable_tools = self.get_config('enable_tools', False)
+        fallback_models = self.get_config('fallback_models', [])
-        tools_config = self.get_config('tools', [])
+        knowledge_bases = self.get_config('knowledge_bases', [])
+        rerank_model = self.get_config('rerank_model', '')
+        rerank_top_k = self.get_config('rerank_top_k', 5)
+        max_round = self.get_config('max_round', 10)
 
-        # Resolve prompts
+        # Resolve prompts - support both new prompt array format and legacy format
-        system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
+        prompt_array = self.get_config('prompt')
-        user_prompt_template = self.get_config('user_prompt_template')
+        user_prompt = ''  # Initialize for later use in _save_to_conversation_history
-        if user_prompt_template is None:
+        
-            user_prompt_template = '{{input}}'
+        if prompt_array and isinstance(prompt_array, list):
-        user_prompt = self._resolve_template(user_prompt_template, inputs, context)
+            # New format: prompt array like pipeline
+            messages = self._build_messages_from_prompt_array(
+                prompt_array, inputs, context, output_format, json_schema
+            )
+            
+            # Get user input text for knowledge retrieval
+            user_input = inputs.get('input', '')
+            
+            # Knowledge retrieval: enhance user input with RAG context
+            user_input = await self._retrieve_knowledge(
+                user_message_text=user_input,
+                knowledge_bases=knowledge_bases,
+                rerank_model_uuid=rerank_model,
+                rerank_top_k=rerank_top_k,
+            )
+            
+            # Track user_prompt for conversation history
+            user_prompt = user_input
+            
+            # Add user input as last message
+            if user_input:
+                messages.append(provider_message.Message(role='user', content=user_input))
+            
+            # Apply max_round to conversation history
+            conversation_history = context.variables.get('_conversation_history', [])
+            if max_round > 0 and conversation_history:
+                max_messages = max_round * 2
+                if len(conversation_history) > max_messages:
+                    conversation_history = conversation_history[-max_messages:]
+                # Insert conversation history before user input
+                history_messages = []
+                for msg in conversation_history:
+                    if isinstance(msg, dict):
+                        role = msg.get('role', 'user')
+                        content = msg.get('content', '')
+                        history_messages.append(provider_message.Message(role=role, content=content))
+                    elif hasattr(msg, 'role') and hasattr(msg, 'content'):
+                        history_messages.append(provider_message.Message(role=msg.role, content=msg.content))
+                # Insert history before user message
+                if history_messages and len(messages) > 0:
+                    messages = messages[:-1] + history_messages + [messages[-1]]
+        else:
+            # Legacy format: separate system_prompt and user_prompt_template
+            system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
+            user_prompt_template = self.get_config('user_prompt_template')
+            if user_prompt_template is None:
+                user_prompt_template = '{{input}}'
+            user_prompt = self._resolve_template(user_prompt_template, inputs, context)
 
-        # Build system prompt with format instructions
+            # Build system prompt with format instructions
-        system_prompt = self._build_system_prompt_with_format(system_prompt, output_format, json_schema)
+            system_prompt = self._build_system_prompt_with_format(system_prompt, output_format, json_schema)
 
-        # Build messages
+            # Knowledge retrieval: enhance user prompt with RAG context
-        messages: list[provider_message.Message] = []
+            user_prompt = await self._retrieve_knowledge(
-        if system_prompt:
+                user_message_text=user_prompt,
-            messages.append(provider_message.Message(role='system', content=system_prompt))
+                knowledge_bases=knowledge_bases,
-        messages.append(provider_message.Message(role='user', content=user_prompt))
+                rerank_model_uuid=rerank_model,
+                rerank_top_k=rerank_top_k,
+            )
 
-        # Get model
+            # Build messages with conversation history
-        runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
+            messages = self._build_messages_with_history(
+                system_prompt=system_prompt,
+                user_message_text=user_prompt,
+                context=context,
+                max_round=max_round,
+            )
+
+        # Get model candidates (primary + fallbacks)
+        candidates = await self._get_model_candidates(model_uuid, fallback_models)
+        if not candidates:
+            raise ValueError('No valid model candidates available')
 
         # Build extra args from config
         extra_args: dict[str, Any] = {}
@@ -219,25 +493,12 @@ class LLMCallNode(WorkflowNode):
         if max_tokens and int(max_tokens) > 0:
             extra_args['max_tokens'] = int(max_tokens)
 
-        # Build tools list if enabled
+        # Invoke LLM with fallback
-        funcs: list[resource_tool.LLMTool] | None = None
-        if enable_tools and tools_config:
-            try:
-                tool_names = self._parse_tools_config(tools_config)
-                if tool_names:
-                    all_tools = await self.ap.tool_mgr.get_tools()
-                    funcs = [t for t in all_tools if t.name in tool_names]
-            except Exception as e:
-                logger.warning(f'[LLM:{self.node_id}] Failed to load tools: {e}')
-                funcs = None
-
-        # Invoke LLM
         try:
-            result_message = await runtime_model.provider.invoke_llm(
+            result_message, used_model = await self._invoke_with_fallback(
-                query=None,
+                candidates=candidates,
-                model=runtime_model,
                 messages=messages,
-                funcs=funcs,
+                funcs=None,
                 extra_args=extra_args,
             )
         except Exception as e:
@@ -300,11 +561,6 @@ class LLMCallNode(WorkflowNode):
             }
 
         # Extract usage info
-        usage = {
-            'prompt_tokens': 0,
-            'completion_tokens': 0,
-            'total_tokens': 0,
-        }
         if hasattr(result_message, 'usage') and result_message.usage:
             u = result_message.usage
             usage = {
@@ -320,10 +576,20 @@ class LLMCallNode(WorkflowNode):
                 'total_tokens': getattr(u, 'total_tokens', 0) or 0,
             }
 
+        # Save to conversation history
+        self._save_to_conversation_history(
+            context=context,
+            user_message_text=user_prompt,
+            response_text=response_text,
+            max_round=max_round,
+        )
+
         # Build result
         result: dict[str, Any] = {
             'response': response_text,
             'usage': usage,
+            'model_used': used_model.model_entity.name if used_model else None,
+            'model_uuid': used_model.model_entity.uuid if used_model else None,
         }
 
         # Parse JSON output if format is json
@@ -359,18 +625,31 @@ class LLMCallNode(WorkflowNode):
         exception_handling = self.get_config('exception_handling', 'show-error')
         failure_hint = self.get_config('failure_hint', 'Request failed.')
 
-        # Resolve prompts
+        # Resolve prompts - support both new prompt array format and legacy format
-        system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
+        prompt_array = self.get_config('prompt')
-        user_prompt_template = self.get_config('user_prompt_template')
+        if prompt_array and isinstance(prompt_array, list):
-        if user_prompt_template is None:
+            # New format: prompt array like pipeline
-            user_prompt_template = '{{input}}'
+            messages = self._build_messages_from_prompt_array(
-        user_prompt = self._resolve_template(user_prompt_template, inputs, context)
+                prompt_array, inputs, context, 'text', ''  # No format instructions for streaming
+            )
+            
+            # Add user input
+            user_input = inputs.get('input', '')
+            if user_input:
+                messages.append(provider_message.Message(role='user', content=user_input))
+        else:
+            # Legacy format
+            system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
+            user_prompt_template = self.get_config('user_prompt_template')
+            if user_prompt_template is None:
+                user_prompt_template = '{{input}}'
+            user_prompt = self._resolve_template(user_prompt_template, inputs, context)
 
-        # Build messages
+            # Build messages
-        messages: list[provider_message.Message] = []
+            messages = []
-        if system_prompt:
+            if system_prompt:
-            messages.append(provider_message.Message(role='system', content=system_prompt))
+                messages.append(provider_message.Message(role='system', content=system_prompt))
-        messages.append(provider_message.Message(role='user', content=user_prompt))
+            messages.append(provider_message.Message(role='user', content=user_prompt))
 
         # Get model
         runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)

src/langbot/templates/metadata/nodes/call_workflow.yaml

@@ -0,0 +1,79 @@
+# Call Workflow Node Configuration
+name: call_workflow
+label:
+  en_US: Call Workflow
+  zh_Hans: 调用工作流
+category: action
+icon: Workflow
+color: '#8b5cf6'
+description:
+  en_US: Invoke an existing Workflow for processing
+  zh_Hans: 调用现有的工作流进行处理
+
+inputs:
+  - name: variables
+    type: object
+    label:
+      en_US: Variables
+      zh_Hans: 变量
+    description:
+      en_US: Variables to pass to the called workflow
+      zh_Hans: 传递给被调用工作流的变量
+    required: false
+
+outputs:
+  - name: result
+    type: object
+    label:
+      en_US: Result
+      zh_Hans: 结果
+    description:
+      en_US: Workflow execution result (ExecutionContext)
+      zh_Hans: 工作流执行结果（执行上下文）
+  - name: status
+    type: string
+    label:
+      en_US: Status
+      zh_Hans: 状态
+    description:
+      en_US: Workflow execution status
+      zh_Hans: 工作流执行状态
+  - name: error
+    type: string
+    label:
+      en_US: Error
+      zh_Hans: 错误
+    description:
+      en_US: Error message if execution failed
+      zh_Hans: 执行失败时的错误信息
+
+config:
+  - name: workflow_uuid
+    type: workflow-selector
+    required: true
+    label:
+      en_US: Workflow
+      zh_Hans: 工作流
+    description:
+      en_US: Workflow to call
+      zh_Hans: 要调用的工作流
+
+  - name: inherit_variables
+    type: boolean
+    default: true
+    label:
+      en_US: Inherit Variables
+      zh_Hans: 继承变量
+    description:
+      en_US: Whether to inherit current workflow variables
+      zh_Hans: 是否继承当前工作流的变量
+
+  - name: timeout
+    type: integer
+    default: 300
+    label:
+      en_US: Timeout (seconds)
+      zh_Hans: 超时时间（秒）
+    description:
+      en_US: Timeout in seconds
+      zh_Hans: 超时时间（秒）

src/langbot/templates/metadata/nodes/llm_call.yaml

@@ -70,25 +70,73 @@ config:
       en_US: Select the LLM model to use
       zh_Hans: 选择要使用的 LLM 模型
 
-  - name: system_prompt
+  - name: fallback_models
-    type: textarea
+    type: model-fallback-selector
+    required: false
+    default: []
     label:
-      en_US: System Prompt
+      en_US: Fallback Models
-      zh_Hans: 系统提示词
+      zh_Hans: 备用模型
     description:
-      en_US: System prompt to set the model behavior
+      en_US: List of fallback models to try if the primary model fails
-      zh_Hans: 设置模型行为的系统提示词
+      zh_Hans: 主模型失败时尝试的备用模型列表
 
-  - name: user_prompt_template
+  - name: prompt
-    type: textarea
-    required: true
-    default: "{{input}}"
     label:
-      en_US: User Prompt Template
+      en_US: Prompt
-      zh_Hans: 用户提示词模板
+      zh_Hans: 提示词
     description:
-      en_US: User prompt template with variable placeholders
+      en_US: Unless you understand the message structure, please only use a single system prompt
-      zh_Hans: 带有变量占位符的用户提示词模板
+      zh_Hans: 除非您了解消息结构，否则请只使用 system 单提示词
+    type: prompt-editor
+    required: true
+    default:
+      - role: system
+        content: "You are a helpful assistant."
+
+  - name: max_round
+    type: integer
+    required: false
+    default: 10
+    label:
+      en_US: Max Round
+      zh_Hans: 最大回合数
+    description:
+      en_US: The maximum number of previous messages that the agent can remember
+      zh_Hans: 最大前文消息回合数
+
+  - name: knowledge_bases
+    type: knowledge-base-multi-selector
+    required: false
+    default: []
+    label:
+      en_US: Knowledge Bases
+      zh_Hans: 知识库
+    description:
+      en_US: Configure the knowledge bases to use for the agent, if not selected, the agent will directly use the LLM to reply
+      zh_Hans: 配置用于提升回复质量的知识库，若不选择，则直接使用大模型回复
+
+  - name: rerank_model
+    type: rerank-model-selector
+    required: false
+    default: ''
+    label:
+      en_US: Rerank Model
+      zh_Hans: 重排序模型
+    description:
+      en_US: Optional rerank model to improve retrieval quality by re-scoring retrieved chunks
+      zh_Hans: 可选的重排序模型，通过重新评分检索结果来提升检索质量
+
+  - name: rerank_top_k
+    type: integer
+    required: false
+    default: 5
+    label:
+      en_US: Rerank Top K
+      zh_Hans: 重排序保留数量
+    description:
+      en_US: Number of top results to keep after reranking
+      zh_Hans: 重排序后保留的最相关结果数量
 
   - name: temperature
     type: number
@@ -134,26 +182,6 @@ config:
       en_US: JSON schema for structured output validation
       zh_Hans: 用于结构化输出验证的 JSON Schema
 
-  - name: enable_tools
-    type: boolean
-    default: false
-    label:
-      en_US: Enable Tools
-      zh_Hans: 启用工具
-    description:
-      en_US: Allow the model to use function calling tools
-      zh_Hans: 允许模型使用函数调用工具
-
-  - name: tools
-    type: json
-    default: []
-    label:
-      en_US: Tools
-      zh_Hans: 工具
-    description:
-      en_US: Select tools that the model can use
-      zh_Hans: 选择模型可以使用的工具
-
   - name: exception_handling
     type: select
     required: true

web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx

@@ -68,6 +68,7 @@ import {
   DropdownMenuTrigger,
 } from '@/components/ui/dropdown-menu';
 import ModelsDialog from '@/app/home/components/models-dialog/ModelsDialog';
+import PromptEditorComponent from '@/app/home/components/dynamic-form/PromptEditorComponent';
 
 const resolveOptionLabel = (label: unknown, fallback: string): string => {
   if (!label || typeof label !== 'object') return fallback;

web/src/app/home/components/dynamic-form/PromptEditorComponent.tsx

@@ -0,0 +1,131 @@
+import { useState, useEffect } from 'react';
+import { Button } from '@/components/ui/button';
+import { Textarea } from '@/components/ui/textarea';
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from '@/components/ui/select';
+import { Plus, Trash2 } from 'lucide-react';
+import { useTranslation } from 'react-i18next';
+
+interface PromptEntry {
+  role: string;
+  content: string;
+}
+
+interface PromptEditorProps {
+  value: PromptEntry[];
+  onChange: (value: PromptEntry[]) => void;
+}
+
+const ROLE_OPTIONS = [
+  { value: 'system', label: 'System' },
+  { value: 'user', label: 'User' },
+  { value: 'assistant', label: 'Assistant' },
+];
+
+export default function PromptEditorComponent({
+  value,
+  onChange,
+}: PromptEditorProps) {
+  const { t } = useTranslation();
+  const [entries, setEntries] = useState<PromptEntry[]>(
+    Array.isArray(value) && value.length > 0
+      ? value
+      : [{ role: 'system', content: '' }],
+  );
+
+  // Sync with external value changes
+  useEffect(() => {
+    if (Array.isArray(value) && value.length > 0) {
+      setEntries(value);
+    }
+  }, [value]);
+
+  const updateEntries = (newEntries: PromptEntry[]) => {
+    setEntries(newEntries);
+    onChange(newEntries);
+  };
+
+  const handleRoleChange = (index: number, role: string) => {
+    const newEntries = [...entries];
+    newEntries[index] = { ...newEntries[index], role };
+    updateEntries(newEntries);
+  };
+
+  const handleContentChange = (index: number, content: string) => {
+    const newEntries = [...entries];
+    newEntries[index] = { ...newEntries[index], content };
+    updateEntries(newEntries);
+  };
+
+  const handleAddEntry = () => {
+    updateEntries([...entries, { role: 'system', content: '' }]);
+  };
+
+  const handleRemoveEntry = (index: number) => {
+    if (entries.length <= 1) return;
+    const newEntries = entries.filter((_, i) => i !== index);
+    updateEntries(newEntries);
+  };
+
+  return (
+    <div className="space-y-3 w-full">
+      {entries.map((entry, index) => (
+        <div
+          key={index}
+          className="flex gap-2 items-start p-3 rounded-lg border bg-card"
+        >
+          <div className="w-32 flex-shrink-0">
+            <Select
+              value={entry.role}
+              onValueChange={(role) => handleRoleChange(index, role)}
+            >
+              <SelectTrigger className="w-full">
+                <SelectValue />
+              </SelectTrigger>
+              <SelectContent>
+                {ROLE_OPTIONS.map((option) => (
+                  <SelectItem key={option.value} value={option.value}>
+                    {option.label}
+                  </SelectItem>
+                ))}
+              </SelectContent>
+            </Select>
+          </div>
+          <div className="flex-1">
+            <Textarea
+              value={entry.content}
+              onChange={(e) => handleContentChange(index, e.target.value)}
+              placeholder={t('workflows.promptContentPlaceholder', 'Enter prompt content...')}
+              className="min-h-[80px] resize-y"
+              rows={3}
+            />
+          </div>
+          <Button
+            type="button"
+            variant="ghost"
+            size="icon"
+            className="shrink-0 text-muted-foreground hover:text-destructive mt-1"
+            onClick={() => handleRemoveEntry(index)}
+            disabled={entries.length <= 1}
+          >
+            <Trash2 className="size-4" />
+          </Button>
+        </div>
+      ))}
+      <Button
+        type="button"
+        variant="outline"
+        className="w-full border-dashed text-muted-foreground hover:text-foreground"
+        onClick={handleAddEntry}
+      >
+        <Plus className="size-4 mr-1.5" />
+        {t('workflows.addPromptEntry', 'Add Prompt Entry')}
+      </Button>
+    </div>
+  );
+}

web/src/app/home/workflows/WorkflowDetailContent.tsx

@@ -55,7 +55,6 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
 
   const [activeTab, setActiveTab] = useState('editor');
   const [workflow, setWorkflow] = useState<Workflow | null>(null);
-  const [createStep, setCreateStep] = useState<'basic' | 'editor'>('basic');
   const [basicInfo, setBasicInfo] = useState<{
     name: string;
     description: string;
@@ -63,7 +62,7 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
   }>({
     name: '',
     description: '',
-    emoji: '🔄',
+    emoji: '💼',
   });
   const fileInputRef = useRef<HTMLInputElement>(null);
   const [isWebSocketConnected, setIsWebSocketConnected] = useState(false);
@@ -136,8 +135,8 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
           name: basicInfo.name || t('workflows.newWorkflow'),
           description: basicInfo.description,
           emoji: basicInfo.emoji,
-          nodes,
+          nodes: [],
-          edges,
+          edges: [],
         });
         refreshWorkflows();
         navigate(`/home/workflows?id=${encodeURIComponent(resp.uuid)}`);
@@ -330,7 +329,7 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
   }, [workflow, refreshWorkflows, navigate, t]);
 
   // ==================== Create Mode ====================
-  if (isCreateMode && createStep === 'basic') {
+  if (isCreateMode) {
     return (
       <div className="flex h-full flex-col">
         <div className="flex items-center justify-between pb-4 shrink-0">
@@ -352,11 +351,8 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
               <Upload className="size-4 mr-1" />
               {t('workflows.import')}
             </Button>
-            <Button
+            <Button onClick={handleSave} disabled={isSaving || !basicInfo.name.trim()}>
-              onClick={() => setCreateStep('editor')}
+              {isSaving ? t('common.saving') : t('common.create')}
-              disabled={!basicInfo.name.trim()}
-            >
-              {t('common.next')}
             </Button>
           </div>
         </div>
@@ -417,30 +413,6 @@ export default function WorkflowDetailContent({ id }: { id: string }) {
     );
   }
 
-  if (isCreateMode) {
-    return (
-      <div className="flex h-full flex-col">
-        <div className="flex items-center justify-between pb-4 shrink-0">
-          <h1 className="text-xl font-semibold">
-            {t('workflows.createWorkflow')}
-          </h1>
-          <div className="flex gap-2">
-            <Button variant="outline" onClick={() => setCreateStep('basic')}>
-              {t('common.back')}
-            </Button>
-            <Button onClick={handleSave} disabled={isSaving}>
-              {isSaving ? t('common.saving') : t('common.create')}
-            </Button>
-          </div>
-        </div>
-
-        <div className="flex-1 min-h-0">
-          <WorkflowEditorComponent />
-        </div>
-      </div>
-    );
-  }
-
   // ==================== Edit Mode ====================
   return (
     <div className="flex h-full flex-col">

web/src/app/home/workflows/components/workflow-editor/WorkflowEditorComponent.tsx

@@ -457,6 +457,7 @@ function WorkflowEditorInner() {
               },
             }}
             deleteKeyCode={null} // We handle delete manually
+            // proOptions={{ hideAttribution: true }} Fack React Flow , we will never give you money, stop asking me to pay for this amazing library that I use for free and contribute to open source.
           >
             <Background
               gap={15}
@@ -696,3 +697,4 @@ export default function WorkflowEditorComponent() {
     </ReactFlowProvider>
   );
 }
+