# LangChain multi-agent orchestration pipelinefrom langchain.agents import AgentExecutor, create_openai_tools_agentfrom langchain_openai import ChatOpenAIfrom langchain.tools import toolfrom langchain.memory import ConversationBufferMemoryfrom langchain.prompts import ChatPromptTemplate, MessagesPlaceholderfrom langgraph.graph import StateGraph, ENDfrom typing import TypedDict, Annotatedimport operator # ── Tool definitions ────────────────────────────────────── @tooldef search_web(query: str) -> str:    """Search the web for real-time information."""    return tavily_client.search(query, max_results=5) @tooldef analyze_document(content: str) -> dict:    """Extract structured data from unstructured documents."""    chain = extraction_chain | output_parser    return chain.invoke({"content": content}) @tooldef query_database(sql: str) -> list[dict]:    """Execute read-only SQL against the data warehouse."""    with engine.connect() as conn:        return conn.execute(text(sql)).mappings().all() @tooldef send_notification(channel: str, message: str) -> bool:    """Send a notification to Slack or email."""    return notification_client.send(channel=channel, text=message) # ── LangGraph state machine ─────────────────────────────── class AgentState(TypedDict):    messages: Annotated[list, operator.add]    context: dict    iteration: int    should_exit: bool def research_node(state: AgentState) -> AgentState:    response = research_agent.invoke({"messages": state["messages"]})    return {        "messages": [response],        "iteration": state["iteration"] + 1,    } def synthesis_node(state: AgentState) -> AgentState:    response = synthesis_agent.invoke(state)    return {"messages": [response], "should_exit": True} def router(state: AgentState) -> str:    if state["iteration"] >= MAX_ITERATIONS:        return "synthesize"    if has_sufficient_context(state["messages"]):        return "synthesize"    return "research" workflow = StateGraph(AgentState)workflow.add_node("research", research_node)workflow.add_node("synthesize", synthesis_node)workflow.add_conditional_edges("research", router, {    "research": "research",    "synthesize": "synthesize",})workflow.add_edge("synthesize", END)workflow.set_entry_point("research")graph = workflow.compile(checkpointer=memory_saver) # ── CrewAI multi-agent crew ─────────────────────────────── from crewai import Agent, Task, Crew, Process researcher = Agent(    role="Senior Research Analyst",    goal="Uncover comprehensive insights across multiple data sources",    backstory="Expert at synthesizing complex information into intelligence briefs",    tools=[search_web, analyze_document],    llm=ChatOpenAI(model="gpt-4o", temperature=0.1),    memory=True,    verbose=True,) analyst = Agent(    role="Data Pipeline Engineer",    goal="Transform raw findings into validated, structured outputs",    backstory="Specialist in building self-healing ETL pipelines with anomaly detection",    tools=[query_database],    llm=ChatOpenAI(model="gpt-4o"),    allow_delegation=True,) reporter = Agent(    role="Technical Communication Lead",    goal="Produce clear executive-ready reports from complex analysis",    backstory="Converts dense technical findings into stakeholder-ready deliverables",    llm=ChatOpenAI(model="gpt-4o"),) research_task = Task(    description="Research {topic} exhaustively. Verify all claims via multiple sources.",    agent=researcher,    expected_output="Comprehensive research brief with citations and confidence scores",    async_execution=True,) analysis_task = Task(    description="Analyze research findings and generate quantitative insights and risk assessment.",    agent=analyst,    context=[research_task],    expected_output="Structured JSON with metrics, trends, and recommended actions",) report_task = Task(    description="Produce a polished executive summary from the analysis.",    agent=reporter,    context=[research_task, analysis_task],    expected_output="Markdown report with executive summary, findings, and next steps",    output_file="report.md",) crew = Crew(    agents=[researcher, analyst, reporter],    tasks=[research_task, analysis_task, report_task],    process=Process.sequential,    full_output=True,    verbose=True,) # ── AutoGen conversation ────────────────────────────────── import autogen config_list = autogen.config_list_from_json("OAI_CONFIG_LIST") assistant = autogen.AssistantAgent(    name="ai_engineer",    llm_config={"config_list": config_list, "temperature": 0},    system_message="Expert AI engineer. Write production-ready, tested code only.",) critic = autogen.AssistantAgent(    name="code_critic",    llm_config={"config_list": config_list},    system_message="Code reviewer. Identify bugs, security issues, and performance problems.",) user_proxy = autogen.UserProxyAgent(    name="executor",    human_input_mode="NEVER",    max_consecutive_auto_reply=12,    code_execution_config={"work_dir": "workspace", "use_docker": True},    is_termination_msg=lambda x: "DONE" in x.get("content", ""),) groupchat = autogen.GroupChat(    agents=[user_proxy, assistant, critic],    messages=[],    max_round=20,)manager = autogen.GroupChatManager(groupchat=groupchat) # ── n8n webhook integration ─────────────────────────────── async def trigger_pipeline(payload: dict) -> dict:    async with httpx.AsyncClient(timeout=30.0) as client:        response = await client.post(            N8N_WEBHOOK_URL,            json={**payload, "timestamp": datetime.utcnow().isoformat()},            headers={"Authorization": f"Bearer {N8N_API_KEY}"},        )        response.raise_for_status()    return response.json() # ── Semantic Kernel plugin ──────────────────────────────── import semantic_kernel as skfrom semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion kernel = sk.Kernel()kernel.add_service(OpenAIChatCompletion(ai_model_id="gpt-4o")) summarize = kernel.add_function(    prompt="Summarize the following in 3 bullet points: {{$input}}",    plugin_name="text_utils",    function_name="summarize",) # ── Pipeline execution ──────────────────────────────────── async def run_pipeline(query: str) -> PipelineResult:    # Phase 1: parallel research    crew_result = await crew.kickoff_async(inputs={"topic": query})     # Phase 2: graph-based reasoning    state = await graph.ainvoke({        "messages": [HumanMessage(content=query)],        "context": {"crew_output": crew_result.raw},        "iteration": 0,        "should_exit": False,    }, config={"configurable": {"thread_id": session_id}})     # Phase 3: notification    await trigger_pipeline({"result": state["messages"][-1].content})     return PipelineResult(        output=state["messages"][-1].content,        iterations=state["iteration"],        sources=extract_sources(state),    )  # LangChain multi-agent orchestration pipelinefrom langchain.agents import AgentExecutor, create_openai_tools_agentfrom langchain_openai import ChatOpenAIfrom langchain.tools import toolfrom langchain.memory import ConversationBufferMemoryfrom langchain.prompts import ChatPromptTemplate, MessagesPlaceholderfrom langgraph.graph import StateGraph, ENDfrom typing import TypedDict, Annotatedimport operator # ── Tool definitions ────────────────────────────────────── @tooldef search_web(query: str) -> str:    """Search the web for real-time information."""    return tavily_client.search(query, max_results=5) @tooldef analyze_document(content: str) -> dict:    """Extract structured data from unstructured documents."""    chain = extraction_chain | output_parser    return chain.invoke({"content": content}) @tooldef query_database(sql: str) -> list[dict]:    """Execute read-only SQL against the data warehouse."""    with engine.connect() as conn:        return conn.execute(text(sql)).mappings().all() @tooldef send_notification(channel: str, message: str) -> bool:    """Send a notification to Slack or email."""    return notification_client.send(channel=channel, text=message) # ── LangGraph state machine ─────────────────────────────── class AgentState(TypedDict):    messages: Annotated[list, operator.add]    context: dict    iteration: int    should_exit: bool def research_node(state: AgentState) -> AgentState:    response = research_agent.invoke({"messages": state["messages"]})    return {        "messages": [response],        "iteration": state["iteration"] + 1,    } def synthesis_node(state: AgentState) -> AgentState:    response = synthesis_agent.invoke(state)    return {"messages": [response], "should_exit": True} def router(state: AgentState) -> str:    if state["iteration"] >= MAX_ITERATIONS:        return "synthesize"    if has_sufficient_context(state["messages"]):        return "synthesize"    return "research" workflow = StateGraph(AgentState)workflow.add_node("research", research_node)workflow.add_node("synthesize", synthesis_node)workflow.add_conditional_edges("research", router, {    "research": "research",    "synthesize": "synthesize",})workflow.add_edge("synthesize", END)workflow.set_entry_point("research")graph = workflow.compile(checkpointer=memory_saver) # ── CrewAI multi-agent crew ─────────────────────────────── from crewai import Agent, Task, Crew, Process researcher = Agent(    role="Senior Research Analyst",    goal="Uncover comprehensive insights across multiple data sources",    backstory="Expert at synthesizing complex information into intelligence briefs",    tools=[search_web, analyze_document],    llm=ChatOpenAI(model="gpt-4o", temperature=0.1),    memory=True,    verbose=True,) analyst = Agent(    role="Data Pipeline Engineer",    goal="Transform raw findings into validated, structured outputs",    backstory="Specialist in building self-healing ETL pipelines with anomaly detection",    tools=[query_database],    llm=ChatOpenAI(model="gpt-4o"),    allow_delegation=True,) reporter = Agent(    role="Technical Communication Lead",    goal="Produce clear executive-ready reports from complex analysis",    backstory="Converts dense technical findings into stakeholder-ready deliverables",    llm=ChatOpenAI(model="gpt-4o"),) research_task = Task(    description="Research {topic} exhaustively. Verify all claims via multiple sources.",    agent=researcher,    expected_output="Comprehensive research brief with citations and confidence scores",    async_execution=True,) analysis_task = Task(    description="Analyze research findings and generate quantitative insights and risk assessment.",    agent=analyst,    context=[research_task],    expected_output="Structured JSON with metrics, trends, and recommended actions",) report_task = Task(    description="Produce a polished executive summary from the analysis.",    agent=reporter,    context=[research_task, analysis_task],    expected_output="Markdown report with executive summary, findings, and next steps",    output_file="report.md",) crew = Crew(    agents=[researcher, analyst, reporter],    tasks=[research_task, analysis_task, report_task],    process=Process.sequential,    full_output=True,    verbose=True,) # ── AutoGen conversation ────────────────────────────────── import autogen config_list = autogen.config_list_from_json("OAI_CONFIG_LIST") assistant = autogen.AssistantAgent(    name="ai_engineer",    llm_config={"config_list": config_list, "temperature": 0},    system_message="Expert AI engineer. Write production-ready, tested code only.",) critic = autogen.AssistantAgent(    name="code_critic",    llm_config={"config_list": config_list},    system_message="Code reviewer. Identify bugs, security issues, and performance problems.",) user_proxy = autogen.UserProxyAgent(    name="executor",    human_input_mode="NEVER",    max_consecutive_auto_reply=12,    code_execution_config={"work_dir": "workspace", "use_docker": True},    is_termination_msg=lambda x: "DONE" in x.get("content", ""),) groupchat = autogen.GroupChat(    agents=[user_proxy, assistant, critic],    messages=[],    max_round=20,)manager = autogen.GroupChatManager(groupchat=groupchat) # ── n8n webhook integration ─────────────────────────────── async def trigger_pipeline(payload: dict) -> dict:    async with httpx.AsyncClient(timeout=30.0) as client:        response = await client.post(            N8N_WEBHOOK_URL,            json={**payload, "timestamp": datetime.utcnow().isoformat()},            headers={"Authorization": f"Bearer {N8N_API_KEY}"},        )        response.raise_for_status()    return response.json() # ── Semantic Kernel plugin ──────────────────────────────── import semantic_kernel as skfrom semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion kernel = sk.Kernel()kernel.add_service(OpenAIChatCompletion(ai_model_id="gpt-4o")) summarize = kernel.add_function(    prompt="Summarize the following in 3 bullet points: {{$input}}",    plugin_name="text_utils",    function_name="summarize",) # ── Pipeline execution ──────────────────────────────────── async def run_pipeline(query: str) -> PipelineResult:    # Phase 1: parallel research    crew_result = await crew.kickoff_async(inputs={"topic": query})     # Phase 2: graph-based reasoning    state = await graph.ainvoke({        "messages": [HumanMessage(content=query)],        "context": {"crew_output": crew_result.raw},        "iteration": 0,        "should_exit": False,    }, config={"configurable": {"thread_id": session_id}})     # Phase 3: notification    await trigger_pipeline({"result": state["messages"][-1].content})     return PipelineResult(        output=state["messages"][-1].content,        iterations=state["iteration"],        sources=extract_sources(state),    )