Add langgraph integration example, also support thinking.

examples/pipelines/integrations/langgraph_pipeline/README.md

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+# Example of langgraph integration
+## Python version: 3.11
+## Feature
+1. Using langgraph stream writer and custom mode of stream to integrate langgraph with open webui pipeline.
+2. Support \<think\> block display.
+## Prerequirement
+Install the open webui pipeline.
+You can follow the docs : https://docs.openwebui.com/pipelines/#-quick-start-with-docker
+
+## Usage
+### 1. Upload pipeline file
+Upload `langgraph_stream_pipeline.py` to the open webui pipeline. 
+
+### 2. Enable the uploaded pipeline
+Properly set up your langgraph api url.
+
+And choose **"LangGraph stream"** as your model.
+
+### 2. Install dependencies
+Under the folder `pipelines/examples/pipelines/integrations/langgraph_pipeline`, run command below :
+```
+pip install -r requirements.txt
+``` 
+### 3. Start langgraph api server
+Run command below :
+```
+uvicorn langgraph_example:app --reload
+```

examples/pipelines/integrations/langgraph_pipeline/langgraph_example.py

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+"""
+title: Langgraph stream integration
+author: bartonzzx
+author_url: https://github.com/bartonzzx
+git_url: 
+description: Integrate langgraph with open webui pipeline
+required_open_webui_version: 0.4.3
+requirements: none
+version: 0.4.3
+licence: MIT
+"""
+
+
+import os
+import json
+import getpass
+from typing import Annotated, Literal
+from typing_extensions import TypedDict
+
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langchain_openai import ChatOpenAI
+from langgraph.config import get_stream_writer
+
+
+'''
+Define LLM API key
+'''
+def _set_env(var: str):
+    if not os.environ.get(var):
+        os.environ[var] = getpass.getpass(f"{var}: ")
+
+
+_set_env("OPENAI_API_KEY")
+
+
+'''
+Define Langgraph
+'''
+def generate_custom_stream(type: Literal["think","normal"], content: str):
+    content = "\n"+content+"\n"
+    custom_stream_writer = get_stream_writer()
+    return custom_stream_writer({type:content})
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+llm = ChatOpenAI(model="gpt-3.5-turbo")
+
+def chatbot(state: State):
+    think_response = llm.invoke(["Please reasoning:"] + state["messages"])
+    normal_response = llm.invoke(state["messages"])
+    generate_custom_stream("think", think_response.content)
+    generate_custom_stream("normal", normal_response.content)
+    return {"messages": [normal_response]}
+
+# Define graph
+graph_builder = StateGraph(State)   
+
+# Define nodes
+graph_builder.add_node("chatbot", chatbot)
+graph_builder.add_edge("chatbot", END)
+
+# Define edges
+graph_builder.add_edge(START, "chatbot")
+
+# Compile graph
+graph = graph_builder.compile()
+
+
+'''
+Define api processing 
+'''
+app = FastAPI(
+    title="Langgraph API",
+    description="Langgraph API",
+    )
+
+@app.get("/test")
+async def test():
+    return {"message": "Hello World"}
+
+
+@app.post("/stream")
+async def stream(inputs: State):
+    async def event_stream():
+        try:
+            stream_start_msg = {
+                'choices': 
+                    [
+                        {
+                            'delta': {}, 
+                            'finish_reason': None
+                        }
+                    ]
+                }
+
+            # Stream start
+            yield f"data: {json.dumps(stream_start_msg)}\n\n"            
+
+            # Processing langgraph stream response with <think> block support
+            async for event in graph.astream(input=inputs, stream_mode="custom"):
+                print(event)
+                think_content = event.get("think", None)
+                normal_content = event.get("normal", None)
+    
+                think_msg = {
+                    'choices': 
+                    [
+                        {
+                            'delta':
+                            {
+                                'reasoning_content': think_content, 
+                            },
+                            'finish_reason': None                            
+                        }
+                    ]
+                }
+
+                normal_msg = {
+                    'choices': 
+                    [
+                        {
+                            'delta':
+                            {
+                                'content': normal_content, 
+                            },
+                            'finish_reason': None                            
+                        }
+                    ]
+                }
+
+                yield f"data: {json.dumps(think_msg)}\n\n"
+                yield f"data: {json.dumps(normal_msg)}\n\n"
+
+            # End of the stream
+            stream_end_msg = {
+                'choices': [ 
+                    {
+                        'delta': {}, 
+                        'finish_reason': 'stop'
+                    }
+                ]
+            }
+            yield f"data: {json.dumps(stream_end_msg)}\n\n"
+
+        except Exception as e:
+            # Simply print the error information
+            print(f"An error occurred: {e}")
+
+    return StreamingResponse(
+        event_stream(), 
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+        }
+    )
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=9000)

examples/pipelines/integrations/langgraph_pipeline/langgraph_stream_pipeline.py

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+"""
+title: Langgraph stream integration
+author: bartonzzx
+author_url: https://github.com/bartonzzx
+git_url: 
+description: Integrate langgraph with open webui pipeline
+required_open_webui_version: 0.4.3
+requirements: none
+version: 0.4.3
+licence: MIT
+"""
+
+
+import os
+import requests
+from pydantic import BaseModel, Field
+from typing import List, Union, Generator, Iterator
+
+
+class Pipeline:
+    class Valves(BaseModel):
+        API_URL: str = Field(default="http://127.0.0.1:9000/stream", description="Langgraph API URL")
+    
+    def __init__(self):
+        self.id = "LangGraph stream"
+        self.name = "LangGraph stream"
+        # Initialize valve paramaters
+        self.valves = self.Valves(
+            **{k: os.getenv(k, v.default) for k, v in self.Valves.model_fields.items()}
+        )
+
+    async def on_startup(self):
+        # This function is called when the server is started.
+        print(f"on_startup: {__name__}")
+        pass
+    
+    async def on_shutdown(self): 
+        # This function is called when the server is shutdown.
+        print(f"on_shutdown: {__name__}")
+        pass
+
+    def pipe(
+        self, 
+        user_message: str, 
+        model_id: str, 
+        messages: List[dict], 
+        body: dict
+            ) -> Union[str, Generator, Iterator]:
+
+        data = {
+            "messages": [[msg['role'], msg['content']] for msg  in messages],
+            }
+        
+        headers = {
+            'accept': 'text/event-stream',
+            'Content-Type': 'application/json',
+        }
+        
+        response = requests.post(self.valves.API_URL, json=data, headers=headers, stream=True)
+        
+        response.raise_for_status()
+        
+        return response.iter_lines()

examples/pipelines/integrations/langgraph_pipeline/requirements.txt

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+annotated-types==0.7.0
+anyio==4.8.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+distro==1.9.0
+fastapi==0.115.11
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+idna==3.10
+jiter==0.9.0
+jsonpatch==1.33
+jsonpointer==3.0.0
+langchain-core==0.3.45
+langchain-openai==0.3.8
+langgraph==0.3.11
+langgraph-checkpoint==2.0.20
+langgraph-prebuilt==0.1.3
+langgraph-sdk==0.1.57
+langsmith==0.3.15
+msgpack==1.1.0
+openai==1.66.3
+orjson==3.10.15
+packaging==24.2
+pydantic==2.10.6
+pydantic_core==2.27.2
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+requests-toolbelt==1.0.0
+sniffio==1.3.1
+starlette==0.46.1
+tenacity==9.0.0
+tiktoken==0.9.0
+tqdm==4.67.1
+typing_extensions==4.12.2
+urllib3==2.3.0
+uvicorn==0.34.0
+zstandard==0.23.0